ArticlePDF Available

Abstract

The hilsa, Tenualosa ilisha fishery in the northern Bay of Bengal (nBoB) is passing through a crisis manifested by the persistent decline of fish catch in spite of increasing efforts. During the period 2002–2015, the number of boats engaged in the fishery increased by 25% while the hilsa catch decreased by 13%. The exponential value (b) and condition factor (K) of hilsa has also decreased by 46% and 28% respectively. The value of fishing mortality (F = 2.34 year⁻¹) has considerably exceeded the natural mortality (M = 0.56 year⁻¹) during the study period. It is observed that in place of maximum exploitation rate (Emax) of 0.78, the current exploitation rate (E) of hilsa is 0.81 which is above the sustainable limit. It is a matter of serious concern that with 75% probability the first spawners of the population are being targeted by the present fishing practice. The present study observes that the hilsa population of nBoB are being significantly overexploited in the present level of fishing pressure. The maximum sustainable yield (MSY) limit for hilsa is estimated to be around 25,440 tons per year with the corresponding effort (fMSY) that may be deployed to achieve the above mentioned catch ranged from 3571 to 3987 (number of boats). It can be inferred that the hilsa fishery in the nBoB is being unsustainably exploited.
RESEARCH ARTICLE
Present Status of the Sustainable Fishing Limits for Hilsa Shad
in the northern Bay of Bengal, India
Isha Das
1
Sugata Hazra
1
Sourav Das
1
Sandip Giri
1
Sourav Maity
2
Shubhadeep Ghosh
3
Received: 10 July 2017 / Revised: 3 January 2018 / Accepted: 8 January 2018
The National Academy of Sciences, India 2018
Abstract The hilsa, Tenualosa ilisha fishery in the north-
ern Bay of Bengal (nBoB) is passing through a crisis
manifested by the persistent decline of fish catch in spite of
increasing efforts. During the period 2002–2015, the
number of boats engaged in the fishery increased by 25%
while the hilsa catch decreased by 13%. The exponential
value (b) and condition factor (K) of hilsa has also
decreased by 46% and 28% respectively. The value of
fishing mortality (F =2.34 year
-1
) has considerably
exceeded the natural mortality (M =0.56 year
-1
) during
the study period. It is observed that in place of maximum
exploitation rate (E
max
) of 0.78, the current exploitation
rate (E) of hilsa is 0.81 which is above the sustainable limit.
It is a matter of serious concern that with 75% probability
the first spawners of the population are being targeted by
the present fishing practice. The present study observes that
the hilsa population of nBoB are being significantly over-
exploited in the present level of fishing pressure. The
maximum sustainable yield (MSY) limit for hilsa is
estimated to be around 25,440 tons per year with the cor-
responding effort (f
MSY
) that may be deployed to achieve
the above mentioned catch ranged from 3571 to 3987
(number of boats). It can be inferred that the hilsa fishery in
the nBoB is being unsustainably exploited.
Keywords Hilsa Maximum sustainable yield (MSY)
Sustainable fishery Schaefer–Fox model
Introduction
Global average production of hilsa (Tenualosa ilisha;
Hamilton, 1822) is about 4–5 lakh tonnes per year [1].
Annual average production of hilsa in India is *0.4
lakh metric tons per year [2]. In West Bengal, hilsa is an
important component of the state fishery; it accounts
about 11% of the total fish landings [3]. Other than
economic importance, hilsa has important socio-cultural
contribution in the societal milieu of West Bengal [4].
Several scientists have worked on the length–weight
relationship, growth, mortality, spawning pattern and pop-
ulation dynamics of hilsa from different parts of the world
[5,6]. Feeding habits and population dynamics of hilsa in the
Hugli estuary were studied by several workers [7].
The average annual catch of hilsa has decreased by
13% over the past decade [3]. With decreasing fish
catch, concerns are raised on the nature of fishing
pressure acting upon the fish stock i.e. whether the fish
population is being over-harvested. Dutta et al. [7]
reported a propensity of overexploitation of hilsa stock
in the Hugli estuarine region off West Bengal coast. In
the nearby Meghna estuary in Bangladesh, Miah et al.
[8] documented the importance of maximum sustainable
yield (MSY) estimation of hilsa to maintain
Significance statement Hilsa shad have an important contribution in
the economy and socio-cultural milieu of West Bengal. The
decreasing catch trend of hilsa in recent past raised concerns about the
nature of fishing pressure acting upon the fish stock. The present study
enquires about the sustainability of hilsa fishery in the nBoB.
&Isha Das
ishadas2012@gmail.com
1
School of Oceanographic Studies, Jadavpur University, 188,
Raja S. C. Mallik Road, Kolkata, West Bengal 700 032, India
2
Indian National Centre for Ocean Information Services
(INCOIS), Hyderabad, Telangana 500 090, India
3
Visakhapatnam Regional Centre of CMFRI,
Pandurangapuram, Visakhapatnam, Andhra Pradesh 530003,
India
123
Proc. Natl. Acad. Sci., India, Sect. B Biol. Sci.
https://doi.org/10.1007/s40011-018-0963-3
economically viable fishing for a longer period of time.
So, prime objective of the present article is to assess
whether the hilsa fishery in the northern Bay of Bengal
(nBoB) is sustainable or not. For this purpose, the fishing
pressure and MSY limits for hilsa have been estimated
using Schaefer–Fox model for the first time in the nBoB.
Material and Methods
Study Area
Three fish landing centres viz. Digha (2137030.9900N,
8830026.7800E), Diamond Harbour (2111050.8900N,
881208.5500E) and Fraserganj (2134054.8300N,
8815029.6700E) are selected for monthly fish sampling
(Fig. 1) in the West Bengal coastal waters.
Primary Data Collection
Samples are collected randomly from these landing centres.
Length and weight data are recorded on monthly basis
(n =30 or 35, 2009–2016).
Secondary Data Collection
Hilsa catch data and the number of boats operating in the
study area are collected from the Fisheries Statistical
Reports during 2002–2015 [3].
Schaefer–Fox Model
In the present study, Schaefer and Fox Models have been
used to calculate the MSY for the hilsa fish stock in the
nBoB. MSY is the number or weight of a species that can
be harvested from the stock without affecting the survival
of the species in a long term basis [9]. The Schaefer–Fox
model [10,11] is a popular biomass dynamics model used
to study tropical fisheries because it doesn’t need age fre-
quency data to compute the results.
Length–Weight Relationships: Exponential
(b) Values
Length–weight relationships can be used to compare the
life history and morphological characteristics of a species
throughout the previous period [12]. The values of expo-
nent provide information on fish growth. A value of 3
indicates an isometric growth and values other than 3
indicate that the growth of weight is allometric [13]. In the
present study, Anderson and Neumann’s [14] empirical
equation of length–weight relationship has been followed.
Condition Factor
Condition factor (K) is a quantitative parameter which
describes the state of well-being of a fish species and
reflects its feeding condition [15]. In the present study, K of
hilsa is estimated during its peak breeding season from
June to September [16] each year, during 2009–2016. In
the present study, Fulton’s equation is used to determine
the K [15] following Dutta et al. [6].
Fishing Pressure and Population Dynamics Studies
FiSAT II (ver. 1.2.2) software [17] is run to estimate the
values of several parameters used to study the population
dynamics of hilsa stock. The length and weight frequencies
(2012–2016) are used to estimate the Von-Bertalanffy
growth factors (VBGF). The total mortality (Z) is calcu-
lated using the linearized length-converted catch curve
routine. The natural mortality (M) rates are calculated
using Pauly’s empirical equation [18], with mean habitat
temperature (T) of 27.9 C, i.e. mean sea surface temper-
ature of Bay of Bengal [19]. Several studies on mortality of
fishes have been done using this method [20]. Maximum
exploitation rate (E
max
) is also calculated.
Fig. 1 Study area map showing the selected hilsa landing centres
I. Das et al.
123
Results and Discussion
The constants (aand b) of Schaefer model
[Yield =a9Effort ?b9(Effort)
2
], as elaborated in the
Table 1, are estimated using linear regression with CPUE
as the dependent variable (y axis) and total effort as the
independent variable (x axis) (Fig. 2a). Whereas, to
estimate the values of the constants (c and d) of Fox model
[Yield =Effort 9e
(c?d9Effort)
], the logarithmically trans-
formed CPUEs are used (Fig. 2b). The above mentioned
constants have been used to calculate the MSY and f
MSY
i.e. optimum exploitation level. According to the Schaefer
model, the maximum yield that has to be maintained to
achieve a sustainable fishery of hilsa is around 25,440 tons
Table 1 The estimated maximum sustainable yield (MSY) and the effort (f
MSY
) using Schaefer and Fox Model
Year (i) Total marine fish catch (in tonnes) Hilsa (yield) (in tonnes) Y(i) Effort f(i) (x) Schaefer Y(i)/f(i) (y) Fox ln(Y(i)/f(i)) (y)
2002 181,500 32,100 1431 22.43186583 3.11048253
2003 181,600 26,985 2607 10.35097814 2.337081021
2004 179,500 27,256 3285 8.297108067 2.115907028
2005 160,000 19,061 2520 7.563888889 2.023385461
2006 178,098 16,072 2585 6.217408124 1.82735312
2007 182,735 9430 3230 2.919702786 1.071481826
2008 189,290 11,744 4202 2.794859591 1.027781869
2009 179,004 10,560 4821 2.190416926 0.784091903
2010 200,151 60,460 6194 9.761059089 2.278400908
2011 180,201 18,126 6050 2.996033058 1.0972891
2012 147,700 8510 5201 1.636223803 0.492391028
2013 185,095 9407 7066 1.33130484 0.286159544
2014 179,216 9269 3244 2.857274969 1.049868363
2015 173,771 13,405 9108 1.471783048 0.386474623
Mean 4396 5.915707654 1.420582023
SD 2128.171371 5.719545849 0.858664413
Intercept, a or c
1
12.76 2.637
Slope, b or d
1
-0.0016 -0.00028
MSY
Schaefer: -0.25 9a
2
/b
25,440.25
Fox: -(1/d) 9exp(c -1)
18,356.1685
f
MSY
Schaefer: -0.5 9a/b
3987.5
Fox: -(1/d)
3571.428571
MSY maximum sustainable yield, f
MSY
effort to obtain sustainable catch
1
aorborcord=constant
Fig. 2 a Linear regression curve between effort and catch per unit effort (CPUE, y axis) for Schaefer model and blinear regression curve
between effort and log transformed catch per unit effort (lnCPUE, y axis) for Fox model
Present Status of the Sustainable Fishing Limits for Hilsa Shad in the Northern Bay of Bengal
123
and f
MSY
to achieve that catch is estimated to be around
3987 (number of boats). The MSY calculated from the Fox
model is around 18,356 tons and the f
MSY
is 3571. Figure 3
shows the length–weight relationships of the collected fish
samples during the study. All the relationships were
statistically significant (p\0.0001). The exponential val-
ues are estimated as 2.930, 2.982, 2.550, 2.645, 2.438,
2.204, 1.358 and 1.563 from 2009 to 2016 respectively
(Fig. 4). The condition factor, calculated for eight con-
secutive years from 2009 to 2016, presents the values of
Fig. 3 Length–weight relationships of hilsa observed during the study period (2009–2016, n =30 or 35). ahRepresents the individual length–
weight relations for 2009–2016 respectively, irepresent the combined length–weight relationship (p\0.0001) during the study period
I. Das et al.
123
1.648, 1.899, 1.312, 1.466, 1.130, 1.094, 1.111 and 1.182
respectively (shown in Fig. 4).
The annual catch of hilsa from the nBoB, West Bengal
and the corresponding effort (number of boats) deployed
each year (from 2002 to 2014) are compared and shown in
Fig. 5.TheL
?
value is 540.75 mm and the growth rate is
0.58 year
-1
as estimated using the ELEFAN I routine of
FiSAT II software. Figure 6shows the mortality rates and
exploitation rate of hilsa estimated during 2012–2016. The
values of fishing mortality (F) estimated for each year from
2012 to 2016 are 0.65, 0.83, 0.80, 0.83, and 0.81 respec-
tively. The exploitation rates (E) during this time are 0.70,
0.72, 0.71, 0.79, 0.73 and 0.81 respectively. The average F
and M during this period were calculated to be 2.34 and
0.56, respectively. The average exploitation rate (E) of
hilsa in West Bengal coast is estimated as 0.81. Figure 7
shows the maximum exploitation rate (E
max
=0.78) for
hilsa. Figure 8identifies the length classes from the hilsa
population that would have 25%, 50% and 75% chances of
being captured during fishing. It shows that the 269.57 mm
length class of the hilsa population would have 75%
probability of being gilled during fishing in the Hugli
estuary. Table 2shows the comparative study of the pre-
vious works of Bay of Bengal.
Fig. 4 Relative change in condition factors (K) (R
2
=0.7) and
bvalues (R
2
=0.9) of hilsa from 2009 to 2016
Fig. 5 a Increase in effort (No. of boats, R
2
=0.4) and bannual hilsa catch (R
2
=0.7, where hilsa catch data of 2010 is treated as outskirt)
during 2002–2014
Fig. 6 Graph showing the mortality and exploitation (R
2
=0.9;
N=number of fish in a length class, dt =the time needed for the
fish to grow through length class, t =relative age) of hilsa during
2012–2016
Fig. 7 The relative yield per recruit curve (R
2
=0.8) for hilsa from
2012 to 2016
Present Status of the Sustainable Fishing Limits for Hilsa Shad in the Northern Bay of Bengal
123
As discussed earlier, the decadal (2002–2014) decreas-
ing catch trend of hilsa with respect to increasing effort in
the nBoB, West Bengal, makes it imperative to inquire
whether the hilsa fishery in this province is being over
exploited or not. According to Darimont et al. [21] the life
history and morphological traits of wild populations like
fish, can be significantly influenced by human exploitation.
The common phenotypic effects of fishing are reduction in
mean age and size as described by Trippel [22]. Estimate of
K is generally high for a given fish species during its
breeding season [15]. In the present study, the K values for
hilsa during its peak breeding season decreased by 28%
within the time span of 8 years (2009–2016) suggesting
deterioration of the health condition of hilsa in its natural
habitat which may be attributed to overfishing. The bval-
ues for hilsa significantly decreased by 46.6% (from 2.930
in 2009 to 1.563 in 2016). This indicates a change in the
growth pattern of the hilsa population, from isometry to
negative allometry [13]. Hutchings [23] established that the
changes in bvalues can be influenced by variations in
growth rate, length, age, sex and gonadal maturity of a
species. Such life history traits can be sensitive to the
nature of fishing practice [24] designed for selective fish-
ing. Therefore, the population dynamics studies have been
undertaken to identify changes in length structure and
mortality rates of the population.
Amin et al. [5] calculated the growth rate as 0.82 year
-1
for hilsa in Bangladesh coastal waters. Dutta et al. [7]
reported a growth rate of 1.9 year
-1
from nBoB during
2010–2011. In the present study, growth rate is found to be
0.58 year
-1
during 2012–2016. The average length
(R
2
=0.53; p\0.0001) and weight (R
2
=0.54;
p\0.0001) of hilsa is in positively significant relation
with total mortality. While, the natural mortality (M) of
hilsa decline from 1.25 [7] to 0.56 (present study), the total
mortality factor (Z) shows higher value than previous study
[7]. The total mortality (Z) increased by 46.5% from
1.98 year
-1
[7] to 2.90 year
-1
(present study). This was
the effect of higher fishing mortality (F) of hilsa obtained in
the present study.
The probability of capture for different length classes of
hilsa was analysed during 2012–2016. It is observed that
269.57 mm (L
75
) length class has 75% probability of being
captured, when the total mortality was 2.90 year
-1
.As
hilsa attains its first maturity in 250–300 mm length [25],
there is maximum probability that the first spawners would
be caught in the fishery. Dutta et al. [7] estimated a higher
L
75
value (294.03 mm) for hilsa during 2010–2011 indi-
cating 8.3% decrease in the L
75
since then. This shows that
the West Bengal hilsa fishery in the nBoB is targeting
smaller hilsa fish which is unsustainable for the longer
period of time.
Exploitation rate (E) is defined as the fraction of the year
class recruit that is caught in the fishery. It indicates
whether the stock is overfished or not. According to Pauly
[18], sustainable yield is optimized when exploitation rate
reaches 0.5. But, Patterson [26] observed that fishing rate
optimizing the exploitation rate at 0.5 tends to reduce the
fish stock abundance in their natural habitat, hence
exploitation rate of 0.4 was suggested as optimal
exploitation of the stock. In the present study, the
Fig. 8 Graph showing the length classes of hilsa having 25, 50 and
75% probability of capture by the West Bengal fishery
Table 2 The comparative study of the previous works in different parts of Bay of Bengal
Study area Natural mortality
(M) year
-1
Total mortality
(Z) year
-1
Exploitation
rate (E)
Maximum exploitation rate
(E
max
)
L
75
a
(in
mm)
Authors
Northern Bay of
Bengal
0.56 2.90 0.81 0.78 269.57 Present study
Northern Bay of
Bengal
1.25 1.98 0.37 0.555 294.03 Dutta et al. [7]
Bangladesh Coast 1.28 3.77 0.66 0.59 Amin et al. [5]
North-East Coast of
India
0.678 1.71 Reuben et al.
[27]
a
L
75
=length at which 75% of the fish will be vulnerable to the gear
I. Das et al.
123
exploitation rate (E) was estimated to be 0.81, which is
double of the preferred exploitation rate (0.4) and indicates
overexploitation of the hilsa fish stock in this region. The
calculated maximum exploitation rate (E
max
) that can
maximize the yield per recruit of the hilsa fishery is 0.78.
At present the exploitation rate is higher than the maximum
exploitation limit that produce highest yield per each
recruit. Similar observations have been reported by Amin
et al. [5] for other fisheries. So, this can be considered as an
indication of recruitment overfishing of hilsa in the nBoB
region, which fulfils the prime objective of the present
research article. Lower values of E and E
max
were esti-
mated previously in the same study area [7], whereas,
Reuben et al. [27] estimated exploitation rate (E) of 0.58
for hilsa population along the north-east coast of India.
Amin et al. [5] obtained similar results in Bangladesh
waters indicating overexploitation of the fishery. The
higher E
max
value at West Bengal coast also establishes
significant overfishing of hilsa.
According to ICES report [28], the critical threshold
limit has to be defined while management policies are
made to protect fishery industries. Mace [29] described the
transformation of MSY from Target Reference Point (TRP)
to Limit Reference Point (LRP) and its importance in
fishery management. The concept of LRP helps to define
the limit of biomass to be harvested; crossing which
rebuilding plans with definite time frame is needed. In the
present study, the sustainable yield limit for hilsa fishery in
nBoB region of West Bengal has been estimated as max-
imum 25,440 tons annually. The maximum effort (f
MSY
,
number of boats) that may be deployed to achieve this
catch is 3987 boats. These results can be considered as the
limits of catch and effort for a sustainable hilsa fishery in
northern Bay of Bengal. At present, the number of boats
operating in the northern Bay of Bengal is exceeding the
sustainable limit resulting in overexploitation of hilsa
population.
Conclusion
After scrutinizing all the above results, it can be inferred
that the fishing practice of hilsa is unsustainable in nature
in the present study region. To conserve the hilsa popula-
tion in its natural habitat, the hilsa fishery needs suit-
able fishing regulations restricting the number of boats
within 3987 (f
MSY
) and maximum allowable hilsa catch
(MSY) at 25,440 tons per year in the nBoB region.
Acknowledgements The authors express their heartfelt gratitude to
the Indian National Centre for Ocean Information Services (INCOIS)
(INCOIS: F&A: XII: A1:031) for providing funding support to con-
duct the study during 2009–2017. Department of Fisheries, Govt. of
West Bengal and fishermen unions of Fraserganj Fishing harbour,
Diamond Harbour and Digha are gratefully acknowledged for their
support.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict of
interest.
References
1. DoF (2008) Fisheries statistical year book of Bangladesh,
2006–07. Department of Fisheries, MoFL, Dhaka
2. CMFRI (2016) Annual report 2015–16. Central Marine Fisheries
Research Institute, Kochi
3. DoF (2016) Handbook of fisheries statistics 2015–2016. West
Bengal Fisheries Department, Government of West Bengal,
Dhaka
4. Bladon AJ, Katherine MS, Essam YM, Milner-Gulland EJ (2016)
Payments for ecosystem services in developing world fisheries.
Fish Fish 17:839–859
5. Amin SMN, Rahaman MA, Haldar GC, Mazid GC, Milton D
(2002) Population dynamics and stock assessment of hilsa shad,
Tenualosa ilisha in Bangladesh. Asian Fish Sci 15:123–128
6. Dutta S, Maity S, Chanda A, Akhand A, Hazra S (2012) Length
weight relationship of four commercially important marine fishes
of Northern Bay of Bengal, West Bengal, India. J Appl Env Biol
Sci 2:52–58
7. Dutta S, Maity S, Chanda A, Hazra S (2012) Population structure,
mortality rate and exploitation rate of hilsa shad (Tenualosa
ilisha) in West Bengal coast of northern Bay of Bengal, India.
World J Fish Mar Sci 4:54–59
8. Miah MS, Rahman MA, Halder GC, Mazid MA (1998) Estima-
tion of maximum sustainable yield (MSY) of hilsa (Tenualosa
ilisha, Ham.) in the Meghna river of Bangladesh. Bangladesh J
Fish Res 2:177–182
9. Chaloupka M, Balazs G (2007) Using Bayesian state-space
modelling to assess the recovery and harvest potential of the
Hawaiian green sea turtle stock. Ecol Model 205:93–109
10. Schaefer MB (1954) Some aspects of the dynamics of popula-
tions important to the management of commercial marine fish-
eries. Inter-Am Trop Tuna Comm 1:25–56
11. Fox WW Jr (1970) An exponential surplus-yield model for
optimizing exploited fish populations. Trans Am Fish Soc
99:80–88
12. Santos MN, Gaspar MB, Vasconcelos PV, Monteiro CC (2002)
Weight-length relationship for 50 selected fish species of the
Algarve coast (southern Portugal). Fish Res 59:289–295
13. Beverton RJH, Holt SJ (1996) On the dynamics of exploited fish
populations. Chapman and Hall, London
14. Anderson RO, Neumann RM (1996) Length, weight and associ-
ated structural indices. In: Murphy BR, Willis DW (eds) Fisheries
techniques, 2nd edn. American Fisheries Society, Bethesda,
pp 447–482
15. Chow S, Sandifer PA (1991) Differences in growth, morpho-
metric traits and male sexual maturity among Pacific white
shrimp, Penaeus vannamei, from different commercial hatch-
eries. Aquaculture 92:165–179
16. Ahsan DA, Naser MN, Bhaumik U, Hazra S, Bhattacharya SB
(2014) Migration, spawning patterns and conservation of hilsa
shad (Tenualosa ilisha) in Bangladesh and India. IUCN, New
Delhi
17. Gayanilo FC Jr, Sparre P, Pauly D (1996) FAO-ICLARM stock
assessment tools (FiSAT) user’s guide. FAO Computerised
information series (fisheries). Rome, FAO 266
Present Status of the Sustainable Fishing Limits for Hilsa Shad in the Northern Bay of Bengal
123
18. Pauly D (1980) On the inter-relationships between natural mor-
tality, growth performance and mean environmental temperature
in 175 fish stock. J du Conseil 39:175–192
19. Das S, Chanda A, Giri S, Akhand A, Hazra S (2015) Charac-
terizing the influence of tide on the physico-chemical parameters
and nutrient variability in the coastal surface water of the
northern Bay of Bengal during the winter season. Acta Oceano
Sin 34:102–111
20. Mohamed ARM, Qasim AM (2014) Stock assessment and man-
agement of hilsa shad (Tenualosa ilisha) in Iraqi marine waters,
northwest Arabian Gulf. World J Fish Mar Sci 6:201–208
21. Darimont CT, Carlson SM, Kinnison MT, Paquet PC, Reimchen
TE, Wilmers CC (2009) Human predators outpace other agents of
trait change in the wild. Proc Natl Acad Sci USA 106:952–954
22. Trippel EA (1995) Age at maturity as a stress indicator in fish-
eries. Bioscience 45:759–771
23. Hutchings JA (2004) Evolutionary biology: the cod that got
away. Nature 428:899–900
24. Hsieh CH, Reiss CS, Hunter JR, Beddington JR, May RM,
Sugihara G (2006) Fishing elevates variability in the abundance
of exploited species. Nature 443(7113):859–862
25. Bhaumik U, Naskar M, Sharma AP (2011) Size distribution,
length–weight relationship and sex ratio of the Hilsa (Tenualosa
ilisha) in the Hooghly estuarine system. J Inland Fish Soc India
43(2):1–5
26. Patterson K (1992) Fisheries for small pelagic species: an
empirical approach to management targets. Rev Fish Biol Fisher
2:321–338
27. Reuben S, Dan SS, Somarmu MV, Philipose V, Sathianandan TV
(1992) The resources of hilsa shad, Hilsa ilisha (Hamilton), along
the Northeast coast of India. Indian J Fish 39:169–181
28. ICES (1988) Reports of the ICES advisory committee on fishery
management, 1987. ICES Coop Res Rep 153:415
29. Mace PM (2001) A new role for MSY in single species and
ecosystem approaches to fisheries stock assessment and man-
agement. Fish Fish 2:2–31
I. Das et al.
123
... This situation will need ad hoc management plans especially in the context of the expected impacts of climate change (Jose A. Fernandes et al., 2013Fernandes et al., , 2016Lauria et al., 2018;Lotze et al., 2019). However to put in place long lasting resource management plans it is necessary to have a full understanding of the catches (sometimes unreported, see Watson, 2017), local practices in targeting specific fish, based on what are economically important fish species (see e.g. for the case of India, Das et al., 2019;Raman, Sathianandan, Sharma, & Mohanty, 2017;Tarun Kumar &Shivani, 2014, andfor SCS in Pernetta, Ong, Padilla, Rahim, &Chinh, 2013), the marine ecosystem dynamics and the effects due to the role of those species in the food web . Neither one can ignore the aspects that we examine in the second part, related to human development and alternative livelihoods for coastal poor households, since the lack of them is often related to overfishing or unsustainable practices (N. ...
... Neither one can ignore the aspects that we examine in the second part, related to human development and alternative livelihoods for coastal poor households, since the lack of them is often related to overfishing or unsustainable practices (N. Ahmed, Troell, Allison, & Muir, 2010;Das et al., 2019;Dutta, Maity, Chanda, & Hazra, 2012;Hoq, 2007;Teh, Witter, Cheung, Sumaila, & Yin, 2017;Trajano, Gong, Sembiring, & Astuti, 2017). ...
... This leads to projections on the highest losses in terms of profits for low income countries, especially under SSP3 . Furthermore, some specific regions may even find reductions in potential catch also for the sustainable fishing scenarios (see e.g. on tuna, Indian oil sardine, and hilsa in (Das et al., 2020)), so it is strongly advocated that proper management plans to sustain the existing fish stocks should be in place and consider the studied possible futures (Das et al., 2019;Dutta et al., 2012;Pitcher, Kalikoski, Pramod, & Short, 2009;Pomeroy, 2012;Trajano et al., 2017). This needs to be also put in perspective taking into account several studies on the future effects of global change on the deltas, where multiple and interlinked drivers of change are examined (Tessler et al. 2015;Nicholls et al. 2016). ...
Chapter
In this chapter we focus on the future of the coastal economies, in relation to several climate and Shared Socioeconomic Pathways (SSPs) scenarios addressed in the literature. Once understood their major sources of income, especially in rural Asia, highly dependent on agriculture and fisheries, we examine (in this volume on Blue Economy) first the role of fisheries. We discuss the present state and future estimates that some modelling have shown, reflecting on the effects that this may have on those coastal, mostly rural areas mainly depending on them as source of protein and income. Secondly, we look in detail to particularly relevant areas of coastal economies, the deltas, as one of the most likely affected areas of global change. Based on several sources, mainly gridded or downscaled Gross Domestic Product (GDP) and Human Development Index (HDI) measures, we analyze different paths that some of the large deltas in Asia may encounter under different scenarios, often described globally, making use of existing downscaled data. We conclude that those areas tend to be relatively lower endowed than other areas (especially urban) to address futures, especially unsustainable, fragmented and unequal ones.KeywordsShared socioeconomic pathwaysFisheriesDeltaSocio-economicHuman development indexCoastal economiesGross domestic product (GDP)AsiaDynamic interactive vulnerability assessmentImpactAdaptation and vulnerability (IAV)Purchasing power parityVulnerability indexInvestment deficit index
... The life history features of any fish species in a particular habitat determine its long-term sustainability (Das et al., 2019;Kumar et al., 2014;Prasad et al., 2012). Ample information on life history traits, such as sex ratio and size structure, length-weight relationships, growth, conditions, reproduction, and mortality, is crucial for proper planning and management of an exploited stock (Khatun et al., 2022;Gosavi et al., 2019), particularly when the species is a vital constituent of the commercial fisheries and located at the bottom of the upper food chain (Das et al., 2017;Kumar et al., 2014). ...
Full-text available
Article
The river catfish, Eutropiichthys vacha is a vital protein source for rural communities and has high commercial value, but understanding its life history and management strategies reveals major inadequacies and ambiguities in the riverine ecosystems. Consequently, this study employs multi-models to analyze the life history parameters of E. vacha in the Ganges River (northwestern Bangladesh) from January to December, 2020. The total length (TL) and body weight (BW) of 362 individuals (male = 170, female = 192) were measured by a measuring board and a digital weighing balance, respectively. The overall sex ratio (male: female) was 1.0: 1.13 and did not oscillate statistically from the standard 1:1 ratio (p ˃ 0.05). The TL varied from 6.7–19.2 cm for males and 6.3–19.0 cm for females. The length frequency distributions (LFDs) revealed females outnumbered in 8.0–9.99 cm TL whereas males in 7.0–7.99 cm TL. The slope (b) of the length-weight relationship (TL vs. BW) for both sexes (b = 2.87) was substantially lower than isometry, specifying negative allometric growth pattern for E. vacha. Sex-specific relative (KR) and Fulton’s (KF) condition analysis revealed better state of well-being of males than females. Only KF exhibited significant correlation with both BW and TL, hence making it ideal condition for predicting the fitness of E. vacha in this river. Moreover, the relative weight (WR) suggests an imbalanced habitat for females with higher abundance of predators but suitable for males. The form factor (a3.0) was 0.0062 and 0.0065, whereas the size at first maturity (Lm) and mean natural mortality (MW) were 11.38 and 11.27 cm TL and 1.29 and 1.28 year⁻¹ for the respective sexes. Besides, the calculated mean optimum catchable length (Lopt) was 13.58 and 13.09 cm TL for each sex. These findings will be crucial for further studies and to recommend appropriate strategy for the sustainable management of E. vacha in the Ganges River and adjacent watersheds.
... About 42% of areas [87], including Ladakh (J&K), Bundelkh and (U.P.), Purulia (W.B.), Marwar-Mewar (Rajasthan), and many more [88] experience water stress. Simultaneously, increased primary productivity is expected to increase in India's inland waters due to the enhanced thermal regime [89]. When coupled with anthropogenic pollutants and climate change, the chances of eutrophication are anticipated due to decreased DO levels and enhanced nutrient levels [90]. ...
Full-text available
Article
In the modern era, due to urbanization, industrialization, and anthropogenic activities in the catchment, greenhouse gas (GHG; CO2, CH4, and N2O) emissions from freshwater ecosystems received scientific attention because of global warming and future climate impacts. A developing country like India contributes a huge share (4% of global) of GHGs from its freshwater ecosystems (e.g., rivers, lakes, reservoirs) to the atmosphere. This is the first comprehensive review dealing with the GHG emissions from Indian freshwater bodies. Literature reveals that the majority of GHG from India is emitted from its inland water with 19% of CH4 flux and 56% of CO2 flux. A large part of India’s Gross domestic product (GDP) is manipulated by its rivers. As a matter of fact, 117.8 Tg CO2 yr-1 of CO2 is released from its major riverine waters. The potential of GHG emission from Hydropower reservoirs varies between 11-52.9% (mainly CH4 and CO2) because of spatio-temporal variability in the GHG emissions. A significant contribution was also reported from urban lakes, wetlands and other Inland waters. Being a subtropical country, India is one of the GHG hotspots around the world having the highest ratio (GHG: GDP) of 1301.79. Although, a large portion of India’s freshwater has not been considered yet and needs to be accounted for précised regional carbon budgets. Therefore, in this review, GHG emissions from India’s freshwater bodies, drivers behind GHG emissions (e.g., pH, mean depth, dissolved oxygen, and nutrients), and long-term climatic risks are thoroughly reviewed. Besides, research gaps, future directions as well as mitigation measures are being suggested to provide useful insight into the carbon dynamics (sink/source) and control of GHG emissions.
... Microlevel assessment of the stock status of commercially important species at the national level through population dynamics studies using data on length distribution, fish catch, and fishing effort were reported during 1991-1995 by different authors (Pillai et al., 1991;Bennet et al., 1992;James et al., 1992;Reuben et al., 1992;Thiagarajan et al., 1992;Yohannan et al., 1992;Meiyappan et al., 1993;Nair et al., 1993;Rao et al., 1993;Sukumaran et al., 1993). More recently, stock assessments in India have focussed on resources of high commercial value such as shrimps (Chakraborty et al., 2018), cephalopods (Jasmin et al., 2018), and pelagic fishes (Ghosh et al., 2016;Das et al., 2019) without addressing the multigear nature of the fisheries. Reviewing the status of global fisheries, Hilborn et al. (2020) stated that as most unassessed fisheries are in tropical and subtropical regions dominated by highly diverse mixed fisheries, the single-species stock assessment and management practices used in temperate countries are impractical. ...
Conference Paper
The marine fisheries sector in India contribute significantly to the food and nutritional requirements of its people and support livelihood of nearly four million fisher population in addition to foreign exchange earnings through export. Nearly 33 % of the marine fish production is from the southwest coast comprising the states of Kerala, Karnataka and Goa which is only 16.4% of the total coastline. Harvest of the marine fishery resources requires proper control through appropriate management measures for sustainability of the resources. The most commonly used management reference point, maximum sustainable yield (MSY), is derived here for all the important marine fishery resources in these states considering the multi-gear fishery and adopting suitable biomass dynamic modelling approach with time series data on gear wise fish catch and fishing effort as input sourced from the National Marine Fishery Resources Data Centre (NMFDC) of ICAR-Central Marine Fisheries Research Institute. In the modelling approach, the multi-gear situation was handled by incorporating a set of parameters into the observation equation of the biomass dynamics model and all the model parameters, MSY, biomass at MSY level and fishing effort at MSY were estimated for 24 resources in Kerala, 26 resources in Karnataka and 11 resources in Goa. Kobe plots were prepared for each state to see the current status of different marine fishery resources in these states using information on ratios of current levels of fishing effort and biomass in relation to the optimum levels of fishing effort and biomass. In the Kobe plot for Kerala, out of the 24 resources examined 13 were falling in green category (both biomass and fishing effort satisfy conditions for sustainability), 3 in yellow, 6 in red and 2 in orange. In Karantaka, out of the 26 resources modelled 9 were green, 5 were yellow, 9 were red and 3 were orange. In the case of Goa, 5 out the 11 resources were green, 5 yellow and 1 red
... 33 During the last two decades, Hilsa exploitation in the bay has increased substantially, above the sustainable limit. 34 The following section presents a brief account of the conservation and management of Hilsa in the Bay of Bengal to illustrate the existing framework for the management of transboundary fish stocks in the bay. ...
... The Hooghly-Bhagirathi-stretch (HBS) of the Ganga river system stands as one of the wellknown destinations for the species in India (Sahoo et al. 2018). Alteration of river hydrology, climate change effects, juvenile overfishing, loss of spawning/spawning grounds, and nursery habitats and destruction of migratory routes have substantially contributed to the decline in the catch of hilsa (Miah 2015;Das et al. 2019;Hossain et al. 2019a, b;Sajina et al. 2020). As a result, it is hard to maintain the supply with the pace of declining wild catching nowadays, though global demand for hilsa has been growing due to its high nutritional values. ...
Full-text available
Article
Identifying the hormonal signature of gonad maturity and spawning seasonality is essential for upgrading the forecast of recruitment, environmental impacts, and captive maturation. The study evaluated the pattern of gonad development and associated plasma levels of sex steroids and thyroid hormones in both sexes to identify gonadal stage markers and reproductive seasonality in an Indian shad, Tenualosa ilisha. Various stages depicted the cellular organization of ovarian and testicular developments. The ovarian histology revealed hilsa as group synchronous total spawners. The seasonal distribution of distinct maturity stages indicated unsynchronized gonad development within the population. Hilsa exhibited a differential preference of environmental cues in spring and late autumn spawning. However, peak spawning activity took place at similar photoperiod and temperature. The appearance of post-ovulatory follicles (POFs) and late spermiating testicular lobules (LS) marked active spawning in different sexes. The gonadosomatic index (IG) showed temporal, stage-specific variations with a positive correlation with plasma 11-KT (11-ketotestosterone), estradiol (E2) in females or with 17α, 20β-dihydroxy-4-pregnene-3-one (DHP) in males, although the relationship altered temporally for each variable. The positively correlated average plasma 11-KT and E2 values with the number of pre- and post-vitellogenic females serve as hormonal signatures for identifying initiation and completion of gonad growth, respectively. Higher circulating levels of 11-KT compared to T during annual gonad cycles indicate 11-KT as the predominant androgen that controls spermatogenesis in this species. Plasma DHP showed a minor peak in March and a significant elevation in October, synchronized with active spawning in females and males marked as a hormonal signature of spawning irrespective of sex. Plasma T4 showed a positive correlation with IG and spawning females, whereas plasma T3 had a similar correlation with 11-KT and the number of early spermiating males in the population. The results demonstrate hormonal signatures of gonadal recrudescence, maturation, and spawning at the population level for promoting recruitment and artificial propagation of hilsa.
... According to the findings of Das et al. [45] hilsa harvest must be controlled by limiting the number of boats used The role of fear in a time-variant prey-predator model with multiple delays and alternative… Table 3 Dynamics of (1.1) (in Fig. 4) as fear, alternative food, probability, delay in maturity, and delay in harvest vary for harvest to < 3987 and limiting the yield to < 25440 tonnes/year. Hossain et al. [46] discussed the loss to the hilsa population because of the small mesh size of the gill nets used for harvesting (little/immature fishes end up getting caught in these nets). ...
Full-text available
Article
We explore the dynamics of a time-variant prey–predator model of two species of fishes, namely Tenualosa ilisha (prey) and Macrognathus pancalus (predator). The prey exhibits anti-predatory behavior due to fear of predation. The prey equation is also equipped with age-based growth and harvestation terms to investigate the significance of maturity and harvest delay. The prey moves from saline water to freshwater for spawning, during which the predator feeds on an alternative food source. We explore the impact of this food source on the survival of prey. Starting with the sufficient conditions for positivity and boundedness of the solutions to the model, we find conditions for the existence of a stable global solution, periodic and almost periodic solutions. We find the range of existence for prey and predator populations and observe the interdependence between these values. According to our findings, just the fear of predation is enough to drive the prey population low. Even when the prey happens to be in an advantageous scenario, fear can still bring its population down, unless there are no or very few predators.
Article
The exploitation status of Tenualosa ilisha (hilsa) stocks from the riverine and marine region of West Bengal was compared in this study for the first time. The study showed that both the values of exponent and condition factor for hilsa reduced over time when compared with previous studies from the same region. Population dynamics study showed the exploitation rate (E) for the riverine hilsa stock were 0.74, 0.79, and 0.78 during pre-monsoon, monsoon, and post-monsoon, respectively, whereas in marine stock, the values were 0.59, 0.64, and 0.71 during pre-monsoon, monsoon, and post-monsoon indicating higher fishing pressure on the riverine hilsa stock. The riverine fishing mortality rate (F) (pre monsoon, 0.83 year−1; monsoon, 11.1 year−1; post-monsoon, 11.5 year−1) was higher than the marine sector (pre monsoon, 0.33 year−1; monsoon, 0.50 year−1; post-monsoon, 0.41 year−1). The fishery in both these sectors was capturing the first spawners of the hilsa population with a 75% probability. Thus, it was evident that the fishing practice in both the studied regions was unsustainable in nature and the exploitation of hilsa in the riverine part was more intense compared to the marine region. All these changes were causing altered population dynamics and fluctuation in the recruitment pattern of hilsa in West Bengal leading towards the dwindling of annual catch of this species.
Chapter
Growth and mortality parameters of spineless cuttlefish, Sepiella inermis were estimated based on length-frequency data collected from seawater at Ratnagiri from March 2015 to February 2017. Asymptotic length (L∞) and growth coefficient (K) were estimated to be 107 mm and 1.6 yr-1 for Ratnagiri by ELEFAN I (Electronic Length-Frequency Analysis). The instantaneous rate of total mortality (Z), natural mortality (M) and fishing mortality (F) were estimated to be 4.47 yr−1, 3.23 yr−1 and 1.24 yr−1 respectively along the Maharashtra coast. Age at length zero, to was estimated to be 0.00 969 year. S. inermis was found to attain a size of 60, 86, 97 and 103 mm at the end of 6, 12, 18 and 24 months, respectively. Length at first capture was found to be 41.10 mm. The current exploitation ratio (E) was determined to be 0.28.KeywordsSpineless cuttlefish Sepiella inermis Age and growthMortalityExploitation ratio
Full-text available
Article
data were integrated into GIS-based modeling by a multi-criteria decision-making technique (analytical hierarchy process) to generate the habitat suitability maps of juvenile Hilsa. The model-derived information was verified with the indigenous knowledge of the fishers regarding the suitable habitat of juvenile Hilsa by conducting group discussions at 13 locations along the entire study area. The degree of agree-ment/disagreement between the model and the field information was determined by measuring Kendall's tau (0.81-0.96) and Kappa coefficients (0.77-0.86), which indicated a strong agreement. In total, 3.80%, 10.12%, and 31.08% of the total river-estuarine area considered for the present study were identified as highly suitable for juvenile Hilsa during pre-monsoon , monsoon, and post-monsoon seasons, respectively. This mapping can act as baseline information for the policymakers for sustainable Hilsa fishing keeping in view the livelihood of fishers.
Full-text available
Article
The trend of Hilsa shad Tenualosa ilisha fishery for the artisanal sector in the Iraqi marine water, Northwest of the Arabian Gulf was described for the period from November 2012 to October 2013. The data on shad landing, interviews and a questionnaire for the fishermen as well as demo fishing were attained. Shad landings varied from 4t in February to 95t in April. Shad amounts formed 11.44% of the total catch Shad landing correlated negatively with salinity of water. The catch per unit of effort of shad for fishermen who was involved in the questionnaire ranged between 1.3-5.1 kg/h/1000m to 0.02-3.42 kg/h/1000m for demo fishing. There are several reasons behind the proposed reduction in shad landings in recent years, including the decline in discharges of Shatt Al-Arab River, overfishing and no regulations to protect and manage marine resources
Full-text available
Article
The spatial distribution of physico-chemical parameters (sea surface temperature (SST), pH, sea surface salinity (SSS), dissolved oxygen (DO) and Secchi depth) along with filterable nutrients (dissolved inorganic nitrate (DIN), dissolved inorganic phosphate (DIP) and reactive silicate (DSi)) are measured in the winter months of November, December, January and February for four consecutive years from 2009–2010 to 2012–2013 on the shallow continental shelf (<20 m bathymetry) of the coastal waters (up to 18 km away from shoreline) of the northern Bay of Bengal (nBoB) during the highest high tide (HHT) and lowest low tide (LLT) hours for the first time. The variability of the coastal biogeochemical environment is assessed during the HHT and LLT hours and for this purpose, seawater samples are collected from seven different locations of a transect in the coastal region. Physicochemical parameters (except SST) show significant difference in magnitude during the HHT and LLT hours respectively. pH, SSS and DO are found to increase in the HHT hours and vice-versa. The data reveal that during the LLT hours, a relative increase of freshwater input in the nBoB can have elevated the nutrient concentration compared with that observed during the HHT hours. The ratio of nutrient concentration is found to deviate significantly from the Redfield ratio. The abundance of DIP is much higher compared with that of DIN and DSi. The anthropogenic sources of DIP from the upstream flow (especially the domestic effluent of several metropolises) can be mainly attributed behind such an observation. In order to characterize and establish the trend of such variation in such an important bio-climatic region, long-term and systematic ecosystem monitoring in the coastal water of the nBoB northern Bay of Bengal should be carried out throughout the year.
Full-text available
Article
Size distribution, length-weight relationship and sex ratio of Tenualosa ilisha were statistically analyzed for the Hooghly estuarine stock between Frasergunj to Dakshineshwar stretch and Digha. The sampling covered commercial catches only. The software R was utilized and length-weight relationship for male, female and combined were W= 0.0013 L2.57, W= 0.0000038 L3.20 and W= 0.0000064 L3.11 respectively. The predictive sex ratios (Male:Female) at male and female maturity were 52:48 and 38:62 respectively. This indicates no effect of male maturity of sex composition, while on female maturity the population becomes female biased.
Full-text available
Book
This publication is an output of joint research conducted on the importance of migratory and spawning patterns for the conservation of the Hilsa, which falls under the Ecosystems for Life theme of improving understanding of ecosystems and habitats. Hilsa is the national fish of Bangladesh and is also important culturally in West-Bengal, India. It is an important staple food and source of income for millions of people in the region. The focus of this research was prompted by recent serious declines in the Hilsa catch. The objective of the research was to study migration and spawning patterns, methods of fishing, status of, and threats to, Hilsa in the region with a view to enhanced conservation. It also reviewed and assessed how current legislation and policy is affecting the species. The research revealed a number of key issues including over exploitation, siltation in river beds, a decrease in water flow from upstream, fragmentation of the river in the dry season and a need to regularise conservation and protection mechanisms between Bangladesh and India.
Full-text available
Article
Payments for Ecosystem Services (PES) is a powerful economic tool that gives positive conditional incentives for the provision of additional ecosystem services over the status quo, which has been used widely in terrestrial conservation. Interest in the concept of marine PES has recently emerged, but the fluid, transboundary and often common pool nature of marine ecosystems presents challenges for PES design and implementation. Here, we consider the potential role of PES in addressing current gaps in fisheries management. Used in combination with conventional regulatory approaches, PES may increase private sector engagement and generate more sustainable financing for fisheries management whilst spreading accountability throughout the supply chain. The approach is most likely to be feasible and effective in commercially valuable fisheries with: (i) demand for one or more ecosystem service and a threat to supply; (ii) suitable baseline data available and potential management actions underpinned by robust science; (iii) clarity and security of property rights; (iv) capacity for hybrid multi-level governance; (v) capacity for rigorous monitoring, control and surveillance; and (vi) potential for financial sustainability of the scheme. An examination of four contrasting fisheries – Namibian hake, Mozambican shallow-water shrimp, Western and Central Pacific skipjack tuna and Bangladesh hilsa – demonstrates that a developing world fishery will rarely fulfil each of these preconditions a priori, but that the potential for successful application of PES still exists. In practice, PES design will depend on the institutional context and require creative and innovative approaches to the maintenance of conditionality and additionality.