Effects of Set Bagnet fisheries on the shallow coastal ecosystem of the Bay of Bengal

Ocean & Coastal Management 67 (2012) 75e86

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Effects of Set Bagnet fisheries on the shallow coastal ecosystem of the Bay of Bengal Md. Rashed-Un-Nabi, Md. Hadayet Ullah* Institute of Marine Sciences and Fisheries, University of Chittagong, Chittagong 4331, Bangladesh

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 15 July 2012

The coastal ecosystem of the Bay of Bengal (BOB) is described using a mass-balance model of trophic interactions to understand the effects of Set Bag Net on the ecosystem. The BOB model had 14 functional ecological groups; 13 living and one dead (detritus). The result showed that the fishery was heavily exploited and operated at a mean trophic level of 2.45. The SBN fishery was characterized with high fishing mortality rates and large omnivory indices for most of the commercially exploited demersal and pelagic groups. The total primary production requirement for sustainable catch was estimated at 15.11%. However, results of Ecosim simulations elucidated that the key resources, like small demersal, small to medium pelagic fish groups and penaeid shrimps, were likely to show a rapid decline in yields within five years with a twice increase in fishing effort and pressure. In contrast, the palaemonidae and sergested shrimp yields showed an increasing trend as they seem to be able to sustain the high fishing pressure, since their predators are also harvested. In addition to that, cephalopod, which is a nontargeted group for this fishery, is also likely to increase in yield for the time being. The two most drastically affected groups in both fisheries were likely to be shark and small demersal. Continuously increasing fishing effort using SBN will lead to a rapid decline of most of the commercial marine resources and predicted to have a serious effect on the ecosystem functioning of BOB. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Fishing activities have a wide impact spectrum and can lead to imbalances in ecosystem function by direct and indirect effects on fish populations and ecosystems (Jennings and Kaiser, 1998; Pauly et al., 2002). Thus, intensive fishing activities directly affect target species’ population structure, growth, reproduction, distribution and have indirect effects on non-target fish species or invertebrate populations and their associated habitats (Jennings and Lock, 1996). Ecosystems are extremely complex where each species interact biologically, and interconnected through the food webs (Pascual and Dunne, 2006). Therefore, overexploitation of certain species from the ecosystem is bound to affect the entire food web (Pauly et al., 2000). Substantial efforts have been made over the last two decades on modelling multispecies coastal and marine fisheries and their potential fishing impact on the ecosystem (reviewed in Browman and Stergiou, 2004). However, almost all of those focused on the commercial fishery, particularly focussing on the impact of * Corresponding author. Department of Hydrology and Water Resource Management, Ecology Center, Christian-Albrechts-University Kiel, Olshausenstr. 75, 24118 Kiel, Germany. Tel.: þ49 01578 1885296. E-mail address: [email protected] (Md. Hadayet Ullah). 0964-5691/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ocecoaman.2012.07.001

trawling on the ecosystem (Anthony, 1993; Antony et al., 2010; Beddington, 1984; Christensen, 1998; Longhurst and Pauly, 1987; Manickchand-Heileman et al., 2004; Sánchez and Olaso, 2004) while surprisingly little effort has been made to model the impact of artisanal fisheries (Albouy et al., 2010; Arreguın-Sánchez et al., 2004; Hussein et al., 2011). Bangladesh has vast coastal and marine resources with an internal estuarine water area of approximately 9700.45 square miles, up to 18.26 m of baseline depth (BFRSS, 1986) and provides a spectacular setting for coastal fisheries. Coastal and marine fisheries of Bay of Bengal (BOB) contributes at least 20% of total fish production in Bangladesh, more than 90% of which comes from artisanal fishing, with approximately 500,000 people fully and directly dependent on the sector (Ahmad, 2004). Thus, fishery resources play a vital role (6.22% of GDP) in the economy of Bangladesh; provide employment (9%) to its population and the second most important factor (5%) that drives the rate of foreign exchange (DOF, 2002). Because of the tremendous potential of the fisheries sector, scientists and dieticians believe that better health for the people can be ensured more quickly and economically through harnessing the production potentials of this sector. Within the inshore water body of BOB (<10 m depth), Set Bagnet (SBN) contribute significantly (28%) to landings and depict

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Md. Rashed-Un-Nabi, Md. Hadayet Ullah / Ocean & Coastal Management 67 (2012) 75e86

the importance of this fishing practice to artisanal fisheries. About 73% of the SBN production comes from the estuarine set bag net (ESBN) fishery, while the rest comes from the seasonal marine set bag net (MSBN) fishery. Both ESBN and MSBN show characteristics of multispecies gear (Huntington et al., 2007; Islam et al., 1993) and the fisheries do not tend to have any by-catch, as all fish are either used for human consumption or dried for poultry/fish feeds. Therefore, fishers tend to exploit the resources as much as possible. Thus, the combined impact of the destructive fishing of the SBN fishery, the shrimp post larvae (PL) fishery and the shrimp trawl fishery, which targets the PL of tiger shrimp, juveniles of miscellaneous marine fauna and spawning adult shrimp have greatly destabilized the coastal fisheries resource base (Huntington et al., 2007). Some recent fragmentary reports (Hussain and Hoq, 2010; Islam, 2003) suggest that most of the commercially important fish stocks of the BOB are overexploited and are, therefore, under threat, although no reliable estimates as to the exact size of stocks are available. Therefore, this study attempts to determine the impact of SBN gears (ESBN and MSBN) on the ecosystem of BOB with particular emphasis on fisheries. The present contribution is also of relevance in view of the increasing need for managing of the BOB ecosystem, which is currently subjected to strong fishery pressure. Fisheries scientists throughout the world largely agree that they must find ways to account for species interactions. The emerging shift of fisheries research from single-species analysis towards an ecosystem-based

approach requires tools that explicitly account for ecological interactions, especially those of a trophic nature. Two such tools which are employed are Ecopath and Ecosim (Christensen and Pauly, 1992, 1993; Walters et al., 1997). These software packages explicitly describe trophic relationships among marine species and simulate changes over time. Thus, Ecosim is the procedure developed in the Ecopath model to depict future fisheries states. By converting the linear equations of Ecopath models to differential equations, Ecosim provides a dynamic mass-balance approach suitable for simulation (Walters et al., 1997). Therefore, the Ecopath with Ecosim software (version 5.0 Beta) was used in the present study. 2. Materials and methods 2.1. Study area The BOB (Bangladesh part), lies between 20 N and 22 300 N latitude, and 89 280 E and 92 300 E longitudes (Fig. 1) and occupies a water area of 166,000 km2. For the present study, an area of 24,000 km2 (<10 m depth; above the solid line) has been considered, with an average annual sea surface temperature of 28  C. Three of the main subcontinent’s rivers, the Ganga, Brahmaputra and Meghna and their tributaries converge in Bangladesh, carrying about 85% of the freshwater runoff, characterized by distinct seasonal fluctuations (Hussain and Hoq, 2010). Primary production in the BOB is known to be highest during the northeast monsoon.

Fig. 1. Map of the coastal ecosystem of Bay of Bengal. Study area is above the solid black line (modified after Huntington et al., 2007).

Md. Rashed-Un-Nabi, Md. Hadayet Ullah / Ocean & Coastal Management 67 (2012) 75e86

2.2. Ecopath base model of the BOB ecosystem The core routine of Ecopath is based on the parameterization of an assumption of mass balance over an arbitrary period, usually a year. The Ecopath model combines estimate of biomass and food consumption of the various components (species or groups of species) in an aquatic ecosystem with an analysis of flows between the ecosystem elements (Christensen and Pauly, 1992, 1993; Polovina, 1984). The energy balance of each trophic group is given by the basic equation:

Consumption ¼ production þ respiration þ unassimilated food The production of each trophic group is balanced by its predation by other trophic groups in the system, its exports from the system and mortality. The ecosystem is modelled using a set of simultaneous linear equations (one for each group i in the system), i.e. production by (i)all predation on (i)non-predation losses of (i)export of (i)biomass accumulation of (i) ¼ 0, for all (I). This can also be expressed as:

Bi *ðP=BÞi *EEi ¼ Yi þ

X  Bj *ðQ =BÞj *DCij þ Exi

(1)

where Bi ¼ the biomass of prey group i, P/Bi ¼ production/biomass ratio of group i equal to the coefficient of total mortality Z at steady state, EEi ¼ ecotrophic efficiency, Yi ¼ yield (fishery catch); Bj ¼ biomass of predator group j; Q/Bj ¼ food consumption per unit biomass of j, DCji ¼ fraction of i in the diet of j and EXi ¼ export of i. For each functional group, the diet composition and at least three of the four parameters B, P/B, EE, and Q/B must be known to establish the model. The Ecopath model of BOB was constructed using the catch data from Huntington et al. (2007) and Nabi (2007). The catch data for ESBN (2003e2007) and MSBN (1991; 2002e2004) were considered for this study. Therefore, a weighted average method (Gupta, 2000) was used to get a desirable percentage of catch data, where all the source data were combined under different family or species. Finally, approximate annual yield was calculated for every single family or species considering 153,687 tons of fish (landings in 2005e06 under SBN fisheries) (FRSS, 2006) as a standard total yield. The model consisted of 14 functional groups, including over 67 species (Table 1) of which 13 were living and one dead (detritus). These functional groups were categorized based on similarities in habitat, population parameters, feeding habits, physiological behaviour, maximum body size and ecological distribution to obtain homogeneous characteristics among the species within a group. Ecological groups ranged from top predators (e.g. sharks)

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to zooplankton and phytoplankton (Table 1). For construction of Ecopath model the input parameters used were biomass estimates, production over biomass ratios (P/B or total mortality rates), consumption over biomass ratios (Q/B) (Table 1), diet compositions (Ullah et al., 2012) and ecological efficiencies (EE) of some ecological groups. Biomass of fish was estimated from the equation of B ¼ Y/F (Gulland, 1971), where Y is the annual average yield of each group and, F is the fishing mortality coefficient. The annual average yield was calculated on the basis of catch data taken from Huntington et al. (2007), while biomass of phytoplankton, zooplankton and detritus were taken from Mohamed et al. (2005). For cephalopod, the biomass was estimated from the mass balance assumption of Ecopath. The production rates (equal to total mortality rates), and other population dynamics parameters were mostly based on Mustafa (1999) and Nabi (2007) supplemented with some other data sources (Amin et al., 2002; Arreguín-Sánchez et al., 1993; Ashraful, 1998; Christensen and Pauly, 1993; Haputhantri et al., 2008; Islam et al., 1993; Mohamed et al., 2005; Zafar et al., 1997, 1999, 2006). Q/B ratios were estimated for each species from the empirical relationship of Palomares and Pauly (1999) using the user interface of the Fishlbbase (Fröese and Pauly, 2006) database. Diet composition of different groups were taken largely from Fishbase (www. fishbase.org; Fröese and Pauly, 2006) and supplemented with the work of Mohamed et al. (2005). Gear wise estimates of fishery yields were also used as input parameters (Table 2). For more details on parameterization of this ecosystem model we refer the readers to online technical publications by Ullah et al. (2012) in which the author detailed the development of preliminary models for BOB. Reviews of EwE are found in Christensen et al. (2000), Christensen and Pauly (1992, 1993) and Pauly et al. (2000). 2.3. Dynamic simulation and strategy applied for simulation run To assess the possibility of over-fishing, simulation using Ecosim was carried out by increasing the fishing effort by twofold within the next 10 years for SBN (ESBN and MSBN simulated separately) as suggested by Walters et al. (1997). Only a very basic simulation with default settings was carried out. The duration of the simulated run was 10 years. An integration step (per year) for biomass in the ‘fast’ groups was set at the default value of 100 steps per year. The integration method chosen was RungeeKutta (4th order), which was the default method incorporated within the software. Relaxation parameter which is the biomasses change for each integration step was chosen 0.5. The step size used for equilibrium analysis was 0.003. The maximum fishing rate in equilibrium analysis was set at

Table 1 Basic estimates obtained after mass balancing using auto-mass balance routine of Ecopath with input and output data (bold). Group No

Group name

Trophic level

Habitat area

Biomass in habitat area (t/km2)

P/B (/year)

Q/B (/year)

EE

P/Q

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Shark Small pelagic Medium pelagic Medium mesopelagic Medium demersal Small demersal Penaeid shrimp Palaemonidae Sergested shrimp Other crustacean Cephalopods Zooplankton Phytoplankton Detritus

3.85 2.66 2.81 2.9 2.91 2.82 2.36 2.32 2 2.25 2.87 2.11 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.008 0.045 0.026 0.237 0.018 0.045 0.028 0.056 0.452 0.246 0.080 10.000 58.000 9.300

3.275 5.570 3.760 2.180 3.500 4.180 5.630 5.660 6.070 0.900 3.100 21.760 17.541 e

8.500 19.390 15.230 7.830 11.230 14.800 19.200 25.000 26.400 14.500 12.000 119.700 e e

0.042 0.981 0.945 0.963 0.968 0.997 0.929 0.944 0.566 0.876 0.950 0.564 0.950 0.328

0.385 0.287 0.247 0.278 0.312 0.282 0.293 0.226 0.230 0.062 0.258 0.182 e e

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Table 2 Table 2Gear wise calculated weighted average (%) of different family/species for ESBN and MSBN (Gathered and compiled from Huntington et al., 2007). Family/species/group

Average from different years Average from different years Weighted average (%) Weighted average (%) Weighted average (%) ESBN

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67

Shark 0.044 S. tri 1.678 S.taty/S.phassas 1.053 C.dusumieri 3.720 Thryssa spp. 1.476 Tenualosa toli 0.015 T.ilisha 0.594 Other clupeids 2.006 Gobiidae 8.131 Mugilidae 1.686 Coilia ramcarati 0.938 Escualosa thoracata 2.320 Glossogobius giuris 0.506 Gudusia chapra 0.485 Odontamblyopus rubicundus 11.034 Terapon jarbua 2.060 Bregmaceros mcclellandii 0.927 Carangidae 0.365 Pterotolithus maculatus 8.113 Megalaspis cordyla 0.317 Parastromateus niger 0.335 Rastrelliger kanagurta 0.006 Chirocentrus nudus 0.346 0.097 P. argenteus L. savala 2.165 Pampus chinensis 0.078 Ariidae 1.864 Polynemus paradiseus 2.993 E.tetradactylum 0.422 S. domina 0.311 S. sihama 0.177 Sciaenidae 4.039 Lates calcarifer 0.067 Lutjanus johnii 0.177 Acanthopagrus latus 0.151 Platycephalus indicus 0.306 Sillaginopsis panijus 0.672 Strongylura leiura 0.081 Pomadasidae (Grunter) 0.190 H. nehereus 26.673 Cynoglossus cynoglossus 3.145 Secutor insidiator 1.208 Gerres filamentosus 0.304 Ichthyscopus insperatus 0.349 Scatophagus argus 0.070 Upeneus sulphureus 0.019 Uranoscopus sp. 0.013 0.024 Boleophthalmus sp. P. monodon/Japonicus/sesulcatus 2.110 P. indicus/P. merguensis 0.880 M. monoceros 1.368 M. brevicornis 2.134 M. spinulatus 0.504 P. sculptiles 2.060 P. stylifera/P. uncta 2.702 Other penaeids 0.850 P. styliferus/P. tenupes 2.431 M. rosenbergii 0.254 Other palaemonides 5.073 Acetes indicus 21.333 Crab (S. serrata) 2.565 Lobstar 0.057 Squila sp. 0.686 Octopus 0.016 Squid (sapia and loligo) 1.804 Holothorium 0.063 Other invertebrates 1.775

e, No catch has been recorded.

MSBN

ESBN

MSBN

Combined

e e 22.043 2.610 0.573 e 0.010 5.310 0.130 e e e e e e e e 0.495 e e e e e 4.580 35.235 1.480 2.953 0.001 0.016 0.003 0.015 4.158 e e e e e e 0.000 10.085 e e e e e e e e 0.560 0.525 0.070 1.053 1.610 1.980 1.710 e 2.431 e 6.340 1.150 0.755 e e e e e 1.327

0.017 0.667 0.419 1.479 0.587 0.006 0.236 0.797 3.231 0.670 0.373 0.922 0.201 0.193 4.385 0.819 0.368 0.145 3.224 0.126 0.133 0.002 0.137 0.039 0.860 0.031 0.741 1.189 0.168 0.124 0.070 1.605 0.026 0.070 0.060 0.122 0.267 0.032 0.076 10.601 1.250 0.480 0.121 0.139 0.028 0.007 0.005 0.010 0.839 0.350 0.544 0.848 0.200 0.819 1.074 0.338 0.966 0.101 2.016 8.478 1.019 0.023 0.273 0.006 0.717 0.025 0.705

0.000 0.000 8.761 1.037 0.228 0.000 0.004 2.110 0.052 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.197 0.000 0.000 0.000 0.000 0.000 1.820 14.003 0.588 1.174 0.000 0.006 0.001 0.006 1.652 0.000 0.000 0.000 0.000 0.000 0.000 0.076 4.008 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.223 0.209 0.028 0.419 0.640 0.787 0.680 0.000 0.966 0.000 2.520 0.457 0.300 0.000 0.000 0.000 0.000 0.000 0.527

0.017 0.667 9.179 2.516 0.814 0.006 0.240 2.908 3.283 0.670 0.373 0.922 0.201 0.193 4.385 0.819 0.368 0.342 3.224 0.126 0.133 0.002 0.137 1.859 14.864 0.619 1.915 1.190 0.174 0.125 0.076 3.258 0.026 0.070 0.060 0.122 0.267 0.032 14.609 1.250 0.480 0.121 0.139 0.028 0.007 0.005 0.010 1.061 0.558 0.571 1.267 0.840 1.606 1.753 0.338 1.932 0.101 4.536 8.935 1.319 0.023 0.273 0.006 0.717 0.025 1.233

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3 and the number of time steps for averaging results was set at 5. The default value of 2.0 and a medium setting of 0.5 were used for maximum relative feeding time and feeding time factor respectively. One key feature of Ecosim is its ability to allow exploring the implications on system dynamics of different views of how the biomass of different groups in ecosystem is controlled. The two extreme views were ‘predator control’ (also called top-down control) and ‘prey control’ (or bottom up). The default value was used in the present simulation except medium pelagic, medium mesopelagic and sergested shrimp where a lower value 0.3 was used. All other settings (unexplained predation, predator effect on feeding time, density dependant catchability) in Ecosim assume default values. 2.4. Model valuation Uncertainties of input parameters were specified under a “pedigree” in the Ecopath with Ecosim software. The “pedigree index” was calculated to quantify the uncertainty related to the input values in the model (Christensen and Walters, 2004). The key criterion used here is that input estimated from local data (i.e., from the area covered by the model is question) as a rule is better than the data from elsewhere, be it a guesstimate, derived from empirical relationships or derived from other Ecopath models. These requirements are met here by three scales, one for biomass, one for P/B and Q/B estimates, and one for diet composition. The index values for input data scale from 0 for data that are not rooted in local data up to a value of 1 for data that are fully rooted in local data. The pedigree index values are used to calculate an overall pedigree index for a given model. The constructed Ecopath model has a pedigree index of 0.423. The precision of the BOB model output is comparable to that of the input data (Essington, 2007). The pedigree index (0.423) is in the upper part of the range (0.16e0.68) of 150 Ecopath models (Morissette et al., 2006), indicated that the parameter values of the model was based on reliable sources and the model is of acceptable quality (Christensen et al., 2000). 3. Results and discussion 3.1. The fishery in the ecosystem Most of the fish functional groups were represented by several species and their respective localized data, which increased the precision of the estimates; this data appears to represent the BOB ecosystem efficiently. The EE values usually range from zero to 1 for the top predators. The EE of most of the commercial fish species in the BOB ecosystem was greater than 0.90. Unlike present study,

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higher EE values of the BOB fishery was also reported by Mustafa (2003), though that was for commercial trawl fishery. The high value of phytoplankton EE (0.95) shows that most of the total phytoplankton production was utilized in the system, assuming that a very negligible amount went to detritus. EE of zooplankton was moderate (0.564) while the value in detritus was found to be low (0.328). The maximum flows to detritus were from zooplankton and phytoplankton followed by sergested shrimp. The lowest flows were observed for the shark and medium demersal groups. For each group, the flow to the detritus consists of what is egested (the non-assimilated food) and those elements of the group that die of old age, diseases, etc. (sources of ‘other mortality’ or 1-EE) (Christensen et al., 2000). The flow to detritus indicated, that in the ecosystem of BOB, almost all the groups were heavily exploited both in terms of fishing or predatory pressure. Fish groups were highly dependent on lower Trophic Level (TL) groups of which very few were transferred to detritus box. This result also indicated that the system is dominated by small sized low TL fish groups namely small and medium pelagic. These includes Setipinna taty/phassas (9.179%), Odontamblyopus rubicundus (4.385%), Coilia dussumieri (2.516%), Gobiidae (3.283%), other clupeids (2.908%) from small pelagic and to some extent Pterotolithus maculatus (3.224%) of medium pelagic group. The increasing dominance of lower TL species in the coastal water of BOB was also reported by (Khan, 2010; Huntington et al., 2007). In nearly all cases, the net food conversion efficiency is less than 1, the exceptions being in groups with intermediate trophic modes, e.g. groups with symbiotic algae. This efficiency cannot be lower than the gross food conversion efficiency, i.e. the ratio between production and consumption (Christensen et al., 2000). However, the present model agrees with the above statements. The maximum omnivory index (OI) was observed for medium demersal (0.524), followed by cephalopods (0.511) and explains the variance in the trophic level of the prey groups for a consumer (Table 3). The medium demersal group comprises of two of the most abundant families of the BOB fishery, namely Ariidae and Sciaenidae with some other macro-carnivore species. The broad spectrum of food preference by Ariidae (Yaiiez-Arancibia and Lara-Dominguez, 1988), combined with the non-selectivity and tendency to feed larger size organisms by the Sciaenidae family (Stickney et al., 1975) might be the underlying reasons for higher OM index for the medium demersal group. The abundant presence of cephalopods (Octopus vulgaris, Sepia sp., Loligo sp.) (BOBLME, 2004), the absence of their typical predator like marine mammals and available food probably made them an opportunistic group in the ecosystem of BOB. The predatory behaviour of cephalopods and cannibalism, which is density-dependent due to their aggressive predatory and territorial nature, is well described in Ibáñez and Keyl (2010).

Table 3 Key indices of the Ecopath model of BOB coastal ecosystem. Group No.

Group name

Flow to detr. (t/km2/yr)

Net efficiency

Omnivory index

Respiration/assimilation

P/R

R/B

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Shark Small pelagic Medium pelagic Medium mesopelagic Medium demersal Small demersal Penaeid shrimp Palaemonidae Sergested shrimp Other crustacean Cephalopods Zooplankton Phytoplankton Detritus

0.040 0.181 0.085 0.390 0.043 0.134 0.117 0.295 3.576 0.740 0.203 334.353 50.755 0

0.482 0.359 0.309 0.348 0.390 0.353 0.367 0.283 0.287 0.078 0.323 0.227 e e

0.004 0.297 0.341 0.245 0.524 0.456 0.273 0.263 0.098 0.216 0.511 0.111 0 0.142

0.518 0.641 0.691 0.652 0.610 0.647 0.633 0.717 0.713 0.922 0.677 0.773 e e

0.929 0.560 0.446 0.534 0.638 0.546 0.579 0.395 0.403 0.084 0.477 0.294 e e

3.525 9.942 8.424 4.084 5.484 7.660 9.730 14.340 15.050 10.700 6.500 74.000 e e

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However, shark (top predators) and sergested shrimp had low omnivory index values which could be attributed to their high degree of prey specialisation. Escobar-Sánchez (2006) commented that shark could be very selective in choosing prey according to its ambush strategy and food requirements. Moreover, R/A ratio cannot exceed 1 as respiration cannot exceed assimilation. For top predators, whose productions are relatively low, R/A ratio can be expected to be close to 1, while it will tend to be lower for organisms at lower TL. Similarly, higher R/A ratio for top predators were reported by Mohamed et al. (2005) for Arabian Sea of Karnataka and Mustafa (2003) for the BOB ecosystem. The present study also confirmed that the value of R/A for all the groups is less than 1. Production/respiration (P/R) and respiration/biomass (R/B), computationally can take any positive value, although thermodynamic constraints limit the realized range of this ratio to values lower than 1. It could be useful when balancing a model. However, the P/R and R/B ratios satisfied the requirement of a balanced ecosystem model for BOB. 3.2. Mortality indices The mortality coefficient can be split and expressed as a series of instantaneous rates per group, Z ¼ P/B ¼ F þ M2 þ BA/B þ E þ M0. In Ecopath, M1 (other mortality’, caused by diseases, senescence) is not included, as this kind of predation mortality should be treated as an export (included in E). Further, M0 is not entered directly, but is computed from the ecotrophic efficiency, EE. If any component of the system is harvested, a summary of the mortality coefficients can be displayed, which presents total mortality (Z ¼ P/B) and its components. Group wise split mortality rates of different ecological groups of BOB ecosystem (Table 4) showed that there was no shark predation mortality (0.00), which was a realistic estimation. The large pelagic sharks were reported as top predators and distinctly dominating (37%) compared to other pelagic fin fishes (Krajangdara et al., 2008; Roy et al., 2008) in BOB. Mustafa (2003) found a very low fishing mortality (0.08) and no predation mortality (0.00) for sharks. The fishing mortality (M) values were considerably higher in the Ecopath model than predation mortality values among the commercially exploited demersal groups; specifically, medium demersal, small demersal and to some extent medium mesopelagic. Evidence of the destructive nature of ESBN is also reported in the various previous studies (Ahmed, 1979, 1981, 1984; Chowdhury, 1987; Islam et al., 1987) which in turn depicts the underlying cause of higher fishing mortality rates of the demersal finfish resources of this ecosystem. However, model results showed that predation pressure was high on pelagic fisheries resources including small and medium pelagic groups. This fishery resource was assumed to be lightly exploited in the BOB ecosystem compared to heavily exploit demersal fisheries resources (BOBLME, 2004; Chowdhury

et al., 1979; Khan et al., 2003) and therefore model findings support prevailing ideas. Thus, the cause of higher predation values was most probably due to the decrease in fishing pressure and comparatively higher percentage of diet preference by other groups in the ecosystem. In contrast, shrimp groups had similar fishing and predatory pressures, hence showing higher exploitation values in the present model, though Mustafa and Khan (1993) commented that shrimp resources are particularly vulnerable to ESBN. The model result showed higher predation pressure on sergested shrimp which was expected as this group is not susceptible to ESBN gear (Khan et al., 1988; Mustafa and Khan, 1993). However, other crustaceans groups, mostly contributed by Scylla serrata, showed higher fishing mortality, which could be attributed to the susceptibility of this species to ESBN gear. Moreover, S. serrata has become a target species in the estuarine and coastal water of BOB because of its great potential for aquaculture and increasing demand in the local and international markets (Azam et al., 1998). These drivers also lead to over-exploitation of this species in many areas (Begum et al., 2009). The top predator (shark) was assumed to have no predatory pressure. 3.3. Niche overlap and electivity index To determine the extent to which any two groups seek the same prey, the prey overlap was considered by using the Pianka index (Pianka, 1973). However, Hurlbert (1978) and Loman (1986) summarized different types of indices, one presenting the overlap between food types (Prey overlap), and the other between predators (Predator overlap). The prey and predator niche overlaps for the BOB ecosystem indicated that medium pelagic, medium mesopelagic, medium demersal, small demersal and cephalopods have significant prey overlap in their diets with small pelagic. This was depicted in the graphical representation of the overlap indices as size shifted connectance plot (Fig. 2). Maximum prey overlaps were observed for medium demersal, small demersal, and penaeid shrimp. Abundance and availability of prey of suitable size and exhibiting appropriate behaviour appeared to be critical factors in prey choice (Meyer and Smale, 1991). Meyer and Smale (1991) also reported that demersal teleosts fishery predominantly fed upon pelagic prey in the two areas of the Cape coast, South Africa. However, maximum predatory overlap for small pelagic indicated the dynamics of the feeding system in the BOB ecosystem, characterized by different prey group preference, varies with size class and presence of available food resources in the different strata of the ecosystem. The electivity index used is the standardized forage ration advocated by Chesson (1983). In the coastal ecosystem of BOB, prey preference for each group showed the considerable importance for ecosystem functioning (Fig. 3). Small pelagic had a marked

Table 4 Estimated group-wise split mortality rates of different ecological groups. Group. No.

Group name

Prod./biom rate ¼ Z

Fishing mort. rate ¼ F

Predat. mort. rate ¼ M2

Other mort. rate ¼ M0

1 2 3 4 5 6 7 8 9 10 11 12 13

Shark Small pelagic Medium pelagic Medium mesopelagic Medium demersal Small demersal Penaeid shrimp Palaemonidae Sergested shrimp Other crustacean Cephalopods Zooplankton Phytoplankton

3.275 5.570 3.760 2.180 3.500 4.180 5.630 5.660 6.070 0.900 3.100 21.760 17.541

0.139 2.516 1.685 1.615 2.045 2.720 2.404 2.613 1.310 0.145 0.413 0.000 0.000

0.000 2.949 1.868 0.484 1.344 1.447 2.826 2.732 2.128 0.643 2.532 12.265 16.666

3.136 0.105 0.207 0.080 0.111 0.013 0.401 0.315 2.632 0.112 0.155 9.495 0.875

Md. Rashed-Un-Nabi, Md. Hadayet Ullah / Ocean & Coastal Management 67 (2012) 75e86

Fig. 2. Size shifted connectance plot at different trophic levels.

preference on palaemonidae while it avoided phytoplankton significantly. Medium pelagic showed significant preference on small pelagic and the reverse occurred in the case of phytoplankton and detritus. Similarly, in the Arabian sea of Karnataka, small pelagic species were also likely to show a negative preference to phytoplankton and detritus, but preyed significantly on micronekton and large zooplankton (Mohamed et al., 2005). Small demersal showed significant avoidance on phytoplankton, zooplankton and detritus. However, medium and small demersal groups also showed the wide range of prey preference among the fish groups including small pelagic, shrimp, cephalopod and to some extent, small demersal. Adult demersal fishes were also reported to show a broad range of prey preference in the east coast of Peninsular Malaysia, where marked preference was on small marine animals (mainly teleosts) as well as cephalopods, crustaceans, echinoderm and mollusks in their diet (Bachok et al., 2004). Surprisingly, other crustaceans showed a preference for the penaeid group while palaemonidae upon themselves. 3.4. Primary production required Estimates of primary production required by fisheries (PPR) were based on the trophic level of the species caught, the energy

81

transfer efficiency between trophic levels, and the primary productivity of the studied area. The present study showed that the fisheries utilised 15.11% of the total primary production (Table 5). This PPR value corroborated the hypothesis that the fisheries of the BOB used a moderate proportion of the productive capacity of the ecosystem. Some similarly exploited systems e.g., Chesapeake bay (Walters et al., 2005), San Miguel Bay, Philippines (Bundy and Pauly, 2001), North coast of central Java (Silvestre et al., 2003), NW Africa upwelling and Peru upwelling (Jarre-Teichmann, 1998) exhibited PPR values of 20.46%, 14.75%, 11.41%, 12.4%, and 12.36% respectively. This indicated a level of fishing pressure in BOB leading to a highly exploited and less productive fishery. However, some systems mostly in temperate regions exhibit values of PPR from 24.2 to 35.3% (Pauly and Christensen, 1995). In the BOB ecosystem, maximum primary production required for the harvest of groups was observed for medium mesopelagic, small demersal and sergested shrimp. The maximum primary production required for the consumption of groups was observed for lower trophic level groups, e.g., zooplankton, sergested shrimp, and to some extent, medium mesopelagic. 3.5. Simulation of yield Christensen (1998) stated that the Ecosim model can be used to predict ecosystem level changes following changes in fishing pressure. Thus, fishing induced changes to a large extent can explain the changes in ecosystem pools and fluxes over time. The model was successfully applied for the study in Gulf of Thailand. In this model, simulation results of ESBN indicated that with an increase in fishing effort the yield of cephalopod, palaemonidae, other crustacean and sergested shrimp were likely to increase manifold (Fig. 4). Among these four groups, yield of other crustacean was likely to follow an upward trend for the first 4 years and then showed a slight decreasing pattern. All other fish groups were likely to suffer a gradual decline over the next 10 year period. Shark and small demersal were likely to be the two groups with the highest declining trend within this period. In case of MSBN, simulation results indicated that with the increase of fishing effort, the yield of cephalopod, palaemonidae, other crustacean, medium demersal and sergested shrimp will be likely to increase manifold (Fig. 5). Among the above mentioned four groups, other crustaceans were likely to show a positive upward trend for the first 2 years and then couldn’t follow the same progress. In contrast,

Fig. 3. Electivity graph for different prey and predator groups in the coastal ecosystem of BOB.

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Table 5 Estimates of primary production required for harvest of all groups in the BOB coastal ecosystem estimated using network analysis. Group name

1 2 3 4 5 6 7 8 9 10 11 12 13

Shark Small pelagic Medium pelagic Medium mesopelagic Medium demersal Small demersal Penaeid shrimp Palaemonidae Sergested shrimp Other crustacean Cephalopods Zooplankton Phytoplankton Total

No. of paths

850 10 120 120 310 145 10 4 2 10 145 2 0 1726

TL

3.85 2.66 2.81 2.9 2.91 2.82 2.36 2.32 2 2.25 2.87 2.11 1 2.45

palaemonidae was likely to show a slow progress for the first 4 years, but then a rapid upward trend is likely to observe for them. Like ESBN, medium demersal was also likely to show a slightly declining trend for the first 4 years but a positive upward trend has been expected for this group in the subsequent years. Analysis of shark and small demersal in MSBN also depicted the same downward trend as ESBN. Furthermore, all other fish groups were expected to show similar features to ESBN fishery. In addition, one of the most important commercial groups of BOB, penaeid shrimp stocks were likely to show a decline in the yield both for ESBN and MSBN. According to the simulation results, both the ESBN and MSBN showed that sergested shrimps (Acetes indicus), crustaceans and cephalopods were predicted to have an increase in yield. The increasing pattern of sergested shrimp and crustaceans could be presumably attributed to fact that of both of the groups were able to sustain the high increase of fishing pressure (Christensen, 1998). The major predators of these groups in the ecosystem were small and medium demersal and to some extent the pelagic groups. Their decrease in biomass was due to increasing fishing pressure and is likely to have helped to increase the biomass of sergested shrimps, crustaceans and cephalopods. Similar results were observed in the Gulf of Thailand (Christensen, 1998) and the Arabian sea of Karnataka (Mohamed et al., 2005). It’s important to note that A. indicus is characterized by low vulnerability (Cheung et al., 2005), higher fecundity (Deshmukh, 2002), and continuous breeding cycle throughout the year (Amin et al., 2009; Deshmukh, 2002; Zafar et al., 1997). Thus, A. indicus is reported to adopt a maximum reproductive output, compared to other related sergestids from

For harvest

For consumption

PPR/catch.

PPR/Tot PP (%)

PPR/cons.

PPR/Tot PP (%)

93.64 19.95 32.56 35.73 36.94 39.38 13.64 13.07 4.35 43.13 38.04 e e 15.11

0.01 0.21 0.13 1.28 0.13 0.45 0.08 0.18 0.24 0.14 0.12 0 0 2.34

36.08 5.73 8.04 9.95 11.51 11.12 4 2.96 1 2.68 9.83 0.9 e e

0.24 0.47 0.3 1.73 0.22 0.69 0.2 0.38 1.12 0.89 0.88 100.86 0 e

higher latitudes (Amin et al., 2009), thus making them capable to maintain higher recruitment and yield despite the increasing fishing pressure in the shallow coastal water of BOB. Furthermore, the abundant presence of this group in the BOB ecosystem is also reported by Zafar et al. (1997). Pauly (1982) found that shrimp recruitment in the Gulf of Thailand increased with decreasing total (mainly fish) standing stock, i.e. increased fishing pressure reduces the standing stock of predators on shrimp, leading to increased shrimp recruitment. The present study also found the overall decreasing trend for both demersal and pelagic groups, revealing that the same situation may occur in BOB. Khan et al. (1988) also commented that sergested shrimp is less susceptible to ESBN and MSBN which may also be one of the reasons to have a higher yield for this group during the simulated years. The other crustacean group is mostly dominated by S. serrata which is a fast growing, hardy species and can adapt itself to various aquatic conditions, with continuous recruitment throughout the year (Kathirvel and Srinivasagam, 1991; Zafar et al., 2006). This species is also characterized by lower fishing mortalities and lower values of Exploitation level (E) in the coastal water of BOB (Zafar et al., 2006), indicating that more exploitation is possible. Thus, a potential increasing trend for the other crustacean group is expected even under a double increase in current fishing effort. The yield of cephalopods was also found to increase (Figs. 4 and 5) which may be due to the fact that cephalopod was a non-targeted group for this fishery with higher predation mortality. Since overall fish yield was decreased over the course of the model run; an increase in cephalopod is predicted as they were preyed heavily upon by different fish groups in the ecosystem. A similar result was

Fig. 4. Ecosim simulation of yield in important components in the ESBN fishery along BOB coastal ecosystem with varying effort regimes over 10 years.

Md. Rashed-Un-Nabi, Md. Hadayet Ullah / Ocean & Coastal Management 67 (2012) 75e86

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Fig. 5. Ecosim simulation of yield in important components in the MSBN fishery along BOB coastal ecosystem with varying effort regimes over 10 years.

reported by Christensen (1998) in the Gulf of Thailand ecosystem, where in spite of heavy fishing intensity and higher mortality of cephalopods there was little effect on yield. Pauly (1985) also found a similar result in the same ecosystem. Moreover, cephalopod biomass was also reported to increase in the model of the Arabian sea of Karnataka (Mohamed et al., 2005) and similar trophic cascade effect seems to work in BOB. In contrast, the penaeid shrimp stocks are found to be at risk, since a decreasing trend is observed for this group. This might be due to several reasons, including the susceptible nature against SBN, abundance presence in the estuarine areas during juvenile stage, being targeted species by SBN and very high market price (Ahmed et al., 2010; BOBP, 1993; FAO, 2001; Khan, 2010). ESBN is reported to be guilty of predominant catching of very small sized marine fauna and thus extensively catch penaeid shrimp within the size of 2e15 cm, except Penaeus monodon which ranges from 5 to 20 but being caught substantially at the size of 8 cm near the estuarine area (FAO, 2001). ESBN is supposed to be operating frequently in the near shore area of BOB. Since most of the species caught are juvenile or at the immature stage, and penaeid shrimp are usually considered to be mature at the sizes between 10 cm and 18 cm, it can be concluded that ESBN fishery has a negative impact on penaeid shrimp stock resources. Furthermore, most of the marine shrimp species, such as P. monodon, Platycephalus indicus, Megalaspis monoceros, Megalaspis brevicornis, Megalaspis spinulatus, P. sculptilis were reported to be overfished by SBN fishery (BOBP, 1993). The two most drastically affected groups in both the ESBN and MSBN fishery were found to be shark and small demersal. In Bangladesh, shark fishery is a newly introduced single fishery in the coastal ecosystem and contributes around 0.18% of the total fish production in Bangladesh (Roy et al., 2008). In Chittagong and Cox’s Bazar district about 70e100 boats are engaged in the commercial shark fishery. During the last few decades, sharks were captured by gill net, demersal trawling (bottom trawling), shrimp trawling, long line, ESBN and MSBN. However, in the current decade (after 2000), shark fishing activities have been confined to the newly introduced “Shark net” (Roy et al., 2008). Sharks, by nature of their k-selected life history strategy and high position in food webs are more likely to be affected by high fishing pressure (Castro et al., 1999; Stevens et al., 2000). Shark may in fact be indicators of fishing pressure while it has been reported that some 50% of the estimated global catch of chondrichthyans is taken as by-catch (Mohamed and Zucharia, 2009; Stevens et al., 2000). This stock reduction does not appear in official fishery statistics and the situation may be worst in the data poor ecosystem of BOB. Stevens et al. (2000) also commented that the ecosystem responses to the removal of sharks

are complex and fairly unpredictable, and therefore, selective fishing mortality can lead to changes in population dynamics of shark. Furthermore, resilience capability of different shark species and the direct and indirect effects of fisheries may also influence the production of shark. However, a decreasing trend for shark was also observed in the Karnataka model (Mohamed et al., 2005). Hence, more attention needs to be given to this poorly studied group. The small demersal fishery was more vulnerable to ESBN and MSBN (Islam et al., 1993) and can be attributed to the declining pattern of this functional group. Moreover, fishing in shallow areas down to 30 m depth is quite intense and competition between artisanal and industrial operations has increased (Islam et al., 1993; Khan, 1994; Paul et al., 1993; Rahman, 1993). The population parameters of species caught by ESBNs (Khan et al., 1992) note a very high fishing mortality (F) and exploitation rates (E) of most species, particularly small demersal, medium demersal and pelagic fisheries, and the relatively small length at first capture (LC), indicated intense fishing pressure on currently exploited resources as may be gleaned from the results of current assessment. Khan et al. (1997) commented that the growth over fishing is a serious problem due to heavy concentration of push nets and ESBN in near shore waters. It is worth noting that the ESBN fishery has been catching many commercially valuable species also being caught by other marine fishery methods. Furthermore, a negative impact was exerted on the demersal and pelagic finfish resources, which also supports the overall declining trend of these fish groups in the current study. Therefore, it can be concluded that SBN fisheries are an important issue to consider for the sustainable management of estuarine and coastal marine fisheries of BOB. 4. Conclusion The present study confirmed that the artisanal fishery of the shallow coastal ecosystem of BOB is under pressure. Most of the fisheries groups were heavily exploited. The higher PPR value of BOB model corroborates the fact that most commercially important stocks in the area were either fully exploited or overexploited, and landings were expected to decrease with a double increase of current fishing pressure. The result showed that the traditional SBN fishery has become destructive to small penaeid shrimp and most of the finfish group resources. The most important penaeid shrimp stock seems to be in a risk of depletion and will possibly affect not only the livelihoods of the coastal fishers but the whole coastal shrimp sector. This could be catastrophic for the country’s economy. The results of this study revealed that ESBN has negative impacts on the coastal fishery of BOB. Thus, this multispecies ESBN

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gear is considered to be a destructive practice (mostly catches juveniles) in the BOB ecosystem. Therefore, increasing fishing effort is not recommended for the BOB fishery. The level of fisheries impact on the BOB ecosystem was found to be comparable to that of the many exploited coastal ecosystems of the world. The results of this model could contribute significantly to obtain the necessary ecological and economical parameters for more effective management options. To date, management of marine and coastal fisheries of BOB is not well controlled. Monitoring, control and surveillance (MCS) is one of the very few interventions that have been made to manage coastal fisheries of BOB but is only concentrated on the industrial trawl fishery which produces <10% of the catch. The artisanal fishery that produces >90% are still beyond the management MCS (Khan, 2010). Thus, there is an uncontrolled expansion of artisanal fisheries sector, and thereby SBN fisheries are playing the major part to destabilize the coastal resource base of BOB. Therefore, SBN fisheries, specifically ESBN operations, in estuarine as well as marine environments less than 10 m deep, should be restricted and mesh size should be taken in account. Seasonal reduction of the fishing effort of the selected estuarine nursery zone should be identified considering important commercial species and the seasons of their occurrence. Adoption of economically viable alternative fishing methods such as mud crab catching, crab-fattening, hook-and-line fishing, trammel netting, bottom drift gillnetting could also be potential management measures. An assessment report reveals that banning ESBN would result in an increase of 250% in yield (BOBP, 1994). Thus, ensuring proper recruitment of juveniles of both shrimp and finfish will certainly support a greater supply of adult species and increase fisheries yield. However, management of the SBN fishery alone will be insufficient; the interactive fisheries also need to be regulated. Particular interest also need to be taken for prawn post larval fishing, which is reported to be highly damaging to biodiversity and wild fish stocks due to indiscriminate fishing of wild postlarvae, with high levels of by-catch (Ahmed et al., 2010; Khan, 2002). Given this, banning postlarvae fishing in ecologically sensitive areas (e.g. Sundarban) could help the coastal fishery to be sustained as well as stimulate the expansion of the prawn hatchery sector by reducing the pressure of wild postlarvae exploitation, thus increasing wild prawn production (Ahmed et al., 2010). Additionally, establishing marine protected areas (MPAs), introducing a co-management approach and institutional capacity building are also important aspects to consider which may act together to protect the valuable fisheries resources. The MPA concept has been proved effective as part of an ecosystem-based approach to protect coastal ecosystems, biodiversity and artisanal fisheries, in response to the symptoms of overexploitation (Dudley, 2008; Lubchenco et al., 2003; Ward et al., 2001). It’s very important to note that implementing of all these management decisions may abruptly affect the livelihood of coastal fisherfolk in the short-term, however, can ensure greater benefit in the long-term; both environmentally and economically, and thereby socially. Therefore, a substantial effort should be made by the Government to create alternative livelihood options for income generation and food security to cope with the short term difficulties, which could be promoting coastal aquaculture, hatchery industry, expansion of food for work program, development of small cash crops, tree crops, livestock rearing, drying fish, fish marketing as well as subsidy and welfare schemes for vulnerable fishing groups. Finally, the marine fisheries policy requires updating to reflect both the precautionary approach as well as the ecosystem approach. Due to its ability to predict changes resulting from various fishing strategies; Ecosim seems to be a good tool for managing multispecies coastal fishery resources of BOB and should be applied to the whole marine ecosystem of BOB.

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