Research Article Bioeconomics of Commercial Marine...

Research ArticleBioeconomics of Commercial Marine Fisheries ofBay of Bengal Status and Direction

Ahasan Habib1 Md Hadayet Ullah2 and Nguyen Ngoc Duy3

1 Department of Biology Faculty of Science Universiti Brunei Darussalam Jalan Tungku LinkGadong 1410 Brunei Darussalam

2WorldFish House No 22B Road No 7 Block F Banani Dhakandash1213 Bangladesh3The Norwegian College of Fishery Science University of Tromso 9037 Tromso Norway

Correspondence should be addressed to Ahasan Habib habibuitgmailcom

Received 15 December 2013 Revised 24 February 2014 Accepted 24 February 2014 Published 3 April 2014

Academic Editor Thanasis Stengos

Copyright copy 2014 Ahasan Habib et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The fishery of the Bay of Bengal (BOB) is assumed to be suffering from the overexploitation This paper aims to assess thesustainability of current level of fishing effort as well as possible changes driven by anthropogenic and climate driven factorsTherefore the commercial marine fishery of BOB for the period of 198586 to 200708 is analyzed by applying Gordon-SchaeferSurplus Production Model on time series of total catch and standardized effort Static reference points such as open-accessequilibrium maximum economic yield and maximum sustainable yield are established Assumptions about potential climatic andanthropogenic effects on r (intrinsic growth rate) and K (carrying capacity) of BOB fishery have been made under three differentreference equilibriums The results showed that the fishery is not biologically overexploited however it is predicted to be passinga critical situation in terms of achieving reference points in the near future But on the other hand economic overfishing startedseveral years before Higher fishing effort and inadequate institutional and legal framework have been the major bottlenecks forthe proper management of BOB fisheries and these may leads fishery more vulnerable against changing marine realm Thus thepresent study calls for policy intervention to rescue the stock from the existing high fishing pressure that would lead to depletion

1 Introduction

Marine wild capture fisheries are crucial to the food andlivelihood security for millions of people supplying approx-imately 53 of fish food among the 110 million tonnes ofwild capture fisheries and aquaculture [1] Globally nearly 170million people are employed in the primary fish productionsecondary processing and marketing sectors [1] and thus theimportance of fish to human food security and the burgeon-ing human population had placed marine fish populationsunder considerable stress [2] At the same time the relativeeconomic efficiency of the fishing industry has significantlydeclined in many countries due to overexploitation and theconsequent reduced yield from fish stocks [3] Thereforethere is an increasing call to conserve and manage com-mercial fishery resources by developing proper managementmeasures using time series data to ensure sustainability andeconomic efficiency for certain fishery However overfishing

pollution and other anthropogenic causes were thought tobe major causes behind underperforming of global marinefisheries [4] Recent climate change is supposed to makethe situation more complicated for global fisheries as it hasbegun to alter ocean conditions particularly water temper-ature and biogeochemistry [5ndash7] Climate change will likelyaffect the economics of fishing through changes in the priceand value of catches fishing costs fishersrsquo incomes earningsof fishing companies discount rates and economic rent aswell as throughout the global economy [5 8]

In Bangladesh the fisheries are the second largestemploying sector involving 13 million people or about 8 ofthe total labor force of the country aswell as the second largestexport sector [9] The countryrsquos marine fisheries have twosubsections artisanal and industrial fisheries [10] contribut-ing to approximately 92 and 726 of the total marine pro-duction respectively [9] However after the independence ofBangladesh in 1971 industrial trawl fisheries suffered from

Hindawi Publishing CorporationEconomics Research InternationalVolume 2014 Article ID 538074 10 pageshttpdxdoiorg1011552014538074

2 Economics Research International

poor investment because of the lack of knowledge and infor-mation on the availability of the size of different fish stocksThough some reports are available for describing biologicaleconomical and resource management issues of the fisheriesof the Bay of Bengal (BOB) [10ndash12] a proper bioeconomicanalysis of the commercial trawl fisheries is scarce exceptfew [3 10] mostly dealing with shrimp fishery resourcesHowever a proper understanding of bioeconomic resourcesand its utilization is an urgent need to promote sustainabledevelopment ofmarine fisheries resources of BOB To controlthe stock catching and fishing effort of the fishery and to getprotection from overexploitation required strong scientificresearch in the field of fisheries biology and economics thatcan be easily examinedwith the help of a suitablemodel usingthe empirical data of the resource Bioeconomic modellinghas long been advocated as an important tool in managingsingle as well as multispecies fisheries for sustainable fisheriesmanagement

Hence the present study is undertaken in the BOBcommercial trawl fishery to assess the sustainability ofmarinefish production and to suggest appropriate policy recom-mendations for improving the capture fisheries scenario ofthe country This study also puts a little effort to analyzethe potential impacts of climate change and anthropogenicdisturbances (eg pollution habitat degradation errors ofestimation due to lack of accurate information and otherunpredictable events) on the fish stock resources To do sothis paper has made some assumptions about climate changeand anthropogenic effects on r (intrinsic growth rate) and K(carrying capacity) in a surplus production model

2 Theoretical Model

Bioeconomic theory in fisheries combines the biologicaland economic aspects of a fishery to explain stock catchand effort dynamics under different regimes and providesinsights into the optimal management of the stock [13]The bioeconomic model provides an integrated approach forevaluation of effective fishery management strategies [14ndash16]

3 Reference Equilibriumsand Management Regimes

The overall goal of fisheries management is to providesustainable biological social and economic benefits fromrenewable aquatic resources For the long-term sustainabilityand for enhancing the revenue of the fishery static as well asthe dynamic behavior of the system should be investigatedby achieving the targeted reference points Maximum eco-nomic yield (MEY) and maximum sustainable yield (MSY)represent different fisheries objectives which are the basisof identifying suitable management measures The otherreference point namely equilibriums open access (OA) ismore likely a regime rather than a performance objective(such as MSY and MEY) Open access represents a lackof property rights to restrict harvesters from common poolresources However OA is not socially efficient (suboptimal)because of its higher effort [17] Moreover this practice can

Tota

l cos

tTo

tal r

even

ue

MSY Total cost

MEYOAY

Total revenue

Fishing effortEMEY EMSY EOAY

Figure 1 The Gordon-Schaefer model

also produce a lower level of catch as compared to the MSYat comparably higher cost Therefore MEY is considered asthe optimal solution since it equates the marginal revenue ofan additional unit of effort However the ldquooptimalrdquo allocationof fishery resources can be determined by bioeconomicmodelling comparingOAMSY andMEY solutions and oftendepends on the objective of particular fisheries management

4 Modelrsquos Choice and Description

In bioeconomic modelling surplus production model whichis an equilibrium model has the capability to determine thesustainable yield from a given fishing effort (Figure 1) andregarded as valuable tool for its first approximation evenin time or data limiting condition [18] These models aregenerally used to examine economic performance or rentdissipation in a fishery [19] and well known in the fisherieseconomics literature [20ndash22] Moreover it is simple and easyto incorporate environmental attributes in the model andits parameters can be estimated using catch and effort dataThe Gordon-Schaefer (GS) model with extension (such ashabitat environmental variables) can potentially identifythe underlying relationship between incorporated variablesstock effort and harvest under open access and maximumeconomic yield managed fisheries [23 24]

The GS model originated from Gordon [17] and Schaefer[25] Therefore the GS surplus production model has beenselected for this study The model has a big advantage ofrequiring limited data and could produce rough guidance onfleet size in the case of single-species as well as multispeciesfishery

Fisheries based on highly productive biological resourceswith large r (intrinsic growth rate) and K (carrying capacity)may sustain a large fishing effort under OA [26] In allpopulations natural surplus growth is small for both highand low stock level and the largest for some intermediatelevel However the GS model is based on the logistic growthequation

119865 (119883) = 119903119883(1 minus119883

119870) (1)

Economics Research International 3

where 119865(119883) is surplus biomass growth per unit of time 119883 isstock biomass The equation describes a parabolic curve as afunction of119883

The harvest rate (119867) is assumed by the simple relation ofSchaefer catch function

119867(119864119883) = 119902119864119883 (2)

where E is fishing effort and q is a constant catchabilitycoefficient Sustainable yield occurs when harvest equals thesurplus growth that is when rate of change of biomass

119889119909

119889119905= 119865 (119883) minus 119867 (119864119883) = 0 (3)

This implies 119902119864119883 = 119903119883(1 minus 119909119870) based on (1) and (2)Therefore biomass at equilibrium119883 is solved to be

119883 = 119870(1 minus119902119864

119903) (4)

Inserting (4) into (2) gives the long-term catch equation

119867(119864) = 119902119870119864(1 minus119902119864

119903) = 119902119870119864 minus

11990221198701198642

119903 (5)

Dividing both sides of (5) by effort (E) gives the linearrelationship between catch per unit of effort (CPUE) andfishing effort

CPUE = 119867119864= 119902119870 minus

1199022119870119864

119903 (6)

Assuming constant price equation (5) can be used to definetotal revenue (TR) in equilibrium as a function of standard-ized effort

TR (119864) = 119901 sdot 119867 (119864) (7)

where p denotes a constant price per unit of harvest Total costof fishing effort (TC) is given by

TC (119864) = 119888 sdot 119864 (8)

where c denotes unit cost of effort including opportunity costof labor and capital

From equations (7) and (8) the equilibrium resource rent(prod) can be derived as a function of fishing effort (E)

prod(119864) = TR (119864) minus TC (119864) (9)

5 Environmental Scenarios

In most surplus production models environmental factorsare found to be ignored over time However this is evidentthat climatic variables as well as anthropogenic factors drivenenvironmental variablity have a notable impact on fisheriesstock and its growth The increasing human activities havebecome amajor factor in progressive environmental degrada-tion on the global scale particularly biological structures andecological processes that mean a reduction in the ecosystemrsquos

carrying capacity [27] Therefore in the present model nineenvironmental scenarios have been considered includingthe present situation (Scenario 0) The scenarios were basedon the current model where each scenario represents pos-sible climate change and anthropogenic consequences Theauthors in [28] described environmental scnerio and possiblegrowth rates As Bangladesh is more vulnerable to climatechange impact therefore we assume that the following ninescenarios are included

(0) current situation ( ie r and K as now)(1) growth rate change by 10 (ie r-10 and K as now)(2) growth rate and carrying capacity both change by 10

(ie r-10 and K-10)(3) carrying capacity change by 10 (ie r as now andK-

10)(4) growth rate change by 25 (ie r-25 andK as now)(5) carrying capacity by 10 and growth rate by 25 (ie

K-10 and r-25)(6) carrying capacity change by 25 (ie r as now and

K-25)(7) growth rate change by 10 and carrying capacity by

25 (ie r-10 and K-25)(8) growth rate and carrying capacity both change by 25

(ie r-25 and K-25)

6 Data and Parameter Estimates

61 Fish Catch and Fishing Effort Data Time-series data(198586 to 200708) on catch and effort of the BOB commer-cial fishery have been gathered and compiled for the presentstudy (Table 1) Data have been collected from the statisticsof Marine Fisheries Department Chittagong (MFDCTG)Bangladesh The catch has been expressed in weight ofbiomass in tonnes and effort has been expressed in termsof fishing days The commercial catch data usually beencollected as fiscal (ie 1985-1986) year by Marine FisheriesDepartment In this study data were presented by both fiscaleconomic year (Table 1) and as the single economic year(text)

The unit price of the harvest and unit cost of fishing effortof the Bay is considered as 50000 BDTand 75000 BDT (1USD= 8175 BDT) respectively in 2007 [29] This fish price is theprice paid in the wholesale market

62 Economic and Statistical Parameters Parameters areestimated by regression of the catch per unit effort data onthe corresponding effort data (Table 1) for the BOB FisheryIn this study in OA equilibrium we have considered thataverage revenue AR = TR119864 is equal to marginal cost (MC =TC1015840(119864)) Therefore from (7) and (8) we get

119901119867

119864= 119888

119867

119864=119888

119901

(10)


Table 1 Total catch and effort data of the Bay of Bengal Bangladeshfrom Fish trawlers (Source Statistics of Marine Fisheries Depart-ment Chittagong Bangladesh)

Year Number oftrawlers

Catch(tonnes)

Effort(days) CPUE

1985-1986 14 5500 1783 30851986-1987 18 4769 2351 20281987-1988 19 4393 2331 18851988-1989 8 931 617 15091989-1990 8 2105 990 21261990-1991 12 1532 721 21251991-1992 14 1974 1421 13891992-1993 12 2545 1545 16471993-1994 11 3305 1228 26911994-1995 14 4404 1354 32531995-1996 12 4568 1432 3191996-1997 14 5793 1656 34981997-1998 13 7515 1856 40491998-1999 18 6680 2136 31271999-2000 21 8017 2517 31852000-2001 31 16027 3871 4142001-2002 36 16586 4841 34262002-2003 42 19428 5414 35882003-2004 49 23207 6284 36932004-2005 68 25895 8535 30342005-2006 78 27096 11469 23632006-2007 88 29446 11462 25692007-2008 95 29176 13368 2183

By using the unit cost of harvest and the resource rent perunit harvest we can find the open-access equilibrium level ofthe fish stock The unit cost of harvest follows by use of (2)and (8)

119862 (119883) =TC (119864)119867=119888119864

119902119864119883=119888

119902119883 (11)

This demonstrates that the unit cost of harvest decreases withan increase in the stock size

With the constant price of fish the resource rent per unitharvest is

119887 (119883) = 119901 minus119888

119902119883 (12)

At the open-access equilibrium the stock level 119883infin

followsfrom 119887 (119883

infin= 0) and open access stock biomass

119883infin=119888

119901119902 (13)

The long-term harvest function can be expressed by

119867(119864) = 119886119864 + 1198871198642 (14)

So CPUE could be expressed by

CPUE = 119886 + 119887119864 (15)

where CPUE = 119867119864 119886 = 119902119870 and 119887 = (minus119886119902119903)Since data on catch and effort are available for the BOB

fishery this allow us to estimate the parameters a and b bylinear regression of the catch per unit of effort on effort

Effort atmaximumsustainable yield can be obtained from(12) by taking the partial derivative ofH with respect to E andsetting it equal to zero as

119864MSY = (minus119886

2119887) (16)

Hence the output at MSY is

MSY = (minus1198862

4119887) (17)

Further on the OA point total fishing costs equal the totalrevenue from the fishery (TR(119864) = TC(119864))Therefore usingthe Gordon-Schaefer model the effort at OA yield can beobtained by equating

MC = AR (18)

or

119888 =119901119867 (119864)

119864

119888119864 = 119901119867 (119864) equiv 119864OAY = (119888119901 minus 119886

119887)

(19)

Themaximum economic return is realized at a lower totalfishing effort for positive economic rent that is only obtainedat efforts lower than 119864OA Maximum economic yield (MEY)is attained at the profit maximizing level of effort which isobtained using (9)prod1015840(119864) = 0 or 119889TR(119864)119889119864 = 119889TC(119864)119889119864Therefore the effort at MEY is

119864MEY = (119888119901 minus 119886

2119887) (20)

Different parameter values and statistical tests of a linearregression on the basis catch and effort data (Table 1) consid-ering (13) have also been analyzed (Table 2) The regressionanalysis reveals that about 60 of the CPUE variation isexplained by the linear model

The results of regression analysis (Table 2) were obtainedfrom time series catch and effort data of the BOB fishery forthe years of 1996 to 2007 It is noteworthy that data collectedsince 1996 is being considered more reliable compared withearlier years which included amore homogenous fleet as wellas a more homogenous catch composition The four majorconcerns motivated to use the shorter time series are asfollows (1) change in accuracy of statistics catch composi-tion has been changed (ie more predators early years) (2)corresponding increase in catch while including more preyspecies (3) changes in size composition (ie decreasingmesh


Table 2 Regression analysis of catch on the corresponding effort data (1996ndash2007) including intercept

Parameters Coefficients Standard error t Stat 119875 value119886 3974 0208 19107 0000119887 minus00001203 00000285 minus422 00018Adjusted 119877 square 0604

0 5000 20000 25000 3000010000 150000

5

10

15

20

25

30

Number of fishing days

Yiel

d in

thou

sand

tonn

es

Figure 2 Gordon-Schaefer harvest curve for the fishery of 1996 to2007 based on (14) and catch data from Table 1

size or similar) and (4) change in operational pattern (ieapproaching other areas longer days) All of these factors areregarded to take a significant shift around 1996 though nosuch changes have been observed over long periods of time

From the estimated coefficients for a and b we can getvalues of K and r respectively as follows

119870 =119886

119902 (21)

119903 =minus119886119902

119887 (22)

7 Results

Intrinsic growth rate and the carrying capacity were calcu-lated based on the estimated coefficients which were derivedfrom (Table 2) the model The GS model has predicted thatthe values of K and r had been 40660 tonnes and 3228 by(21) and (22) respectively and catchability coefficient (119902 =977332lowast10

minus5 in 2003) has been taken from [10]The harvestfunction for the BOB fishery is based on (14) and values ofparameters estimated from Table 2Thus a yield-effort curve(Figure 2) was found to be

119867(119864) = 3974119864 + (minus000012031198642) (23)

Calculation of reference points is the key step towardsapproaching the bioeconomic analysis hence MSY MEYOA corresponding effort levels and economic rent were cal-culated in response to changes in the biological parametersThe values of effort at MSY and MEY were calculated using(16) and (20) while harvests at MSY MEY and OA were cal-culated using this fisheryrsquos harvest equation (14) Economic

rent is the difference between total revenue and total costTherefore total cost and total revenue were calculated using(7) and (8) In order to come up with estimation in changesat various levels of r and K variationwas assumed to rangebetween 10 and 25 A change in K and r mayalso implychanges in harvest corresponding effort and economic levels(Table 3) As indicated in Table 3 MSY was at 32895 tonnesvalued at BDT 406 million and produced at effort value of16517 standard unitsWhen these estimated valueswere com-pared with the recorded catch and effort values (Table 1) ithas been found that the current catch level nearly approachesto MSY value that is obtained from this empirical model Incontrast the MEY was at 28143 tonnes valued at BDT 636million and obtained as effort value of 10282 standard unitsComparing these with the actual catch and effort figuresMEY was attained very recently between 2006 and 2007 TheOAY was at 30854 tonnes and produced at an effort level of20558 standard units which is very close to the catch dataof 2007-2008 considered for the current study In addition tothe present situation eight possible scenarios were presentedin response to potential climate change and anthropogenicinduced variability in the biological parameters of K and are(Table 3)

The parameter value has been changed under each regimewith individual climatic and anthropogenic consequencesAll scenarios have been compared with changing conditionsto quantify the possible impact on the fisheries resourcesIn addition confidence interval (95 level) has showed(Table 3) climate change impacts on stock and effort level atOA MEY and MSY Lower and upper vlue has presentedOnly current scnerio has shown for this change

71 Scenario 0 (Present Situation) This present situationbased on the estimated K and r valued from availablehistorical catch data reveals that the multispecies fisheries ofBOB are in a transition period Since both MSY and MEYwere found to occur within very short and recent times amore integrative approach is needed for intense observationon the yields of the upcoming years However the profit levelwas found to be BDT 636 million at MEY level and BDT 406million at MSY level

72 Scenario 1 In the case of this scenario the average changeor difference was about 10 of harvest levels and nearly 11in profit level compared to the present situation (Scenario 0)

ConsequencesThismay result in a change of BDT63 andBDT44 million at MEY and MSY level respectively compared tothe present scenario


Table 3 Harvest corresponding effort and profit at OA MEY and MSY level in response to changes in the biological parameters K and rwith potential changing climatic and anthropogenic scenarios with confident intervals for current scenario

119870 = 40660mdash25 119903 = 3228mdash25 119903 = 3228mdash10 119903 = 3228

Scenario 8 Scenario 7 Scenario 6119864OA = 12304 119864OA = 14765 119864OA = 16404119864MEY = 6152 119864MEY = 7382 119864MEY = 8202119864MSY = 12385 119864MSY = 14862 119864MSY = 16511119867OA = 18454 119867OA = 22145 119867OA = 24606119867MEY = 13781 119867MEY = 16537 119867MEY = 18375119867MSY = 1856842 119867MSY = 22150 119867MSY = 24607prodMEY = 227 lowast 106 prodMEY = 273 lowast 106 prodMEY = 304 lowast 106

prodMSY = minus6 lowast 106



119870 = 40660mdash10 Scenario 5 Scenario 2 Scenario 3119864OA = 14382 119864OA = 17258 119864OA = 19173119864MEY = 7191 119864MEY = 8629 119864MEY = 9587119864MSY = 12385 119864MSY = 14863 119864MSY = 16511119867OA = 21570 119867OA = 25884 119867OA = 28760119867MEY = 18251 119867MEY = 21901 119867MEY = 24335119867MSY = 22146 119867MSY = 26575 119867MSY = 29528prodMEY = 373 lowast 106 prodMEY = 448 lowast 106 prodMEY = 498 lowast 106

prodMSY = 178 lowast 106 prodMSY = 214 lowast 106 prodMSY = 238 lowast 106

119870 = 40660 Scenario 4 Scenario 1 Scenarioa 0119864OA = 15421 119864OA = 18505 119864OA = 20558 [11350 29779]119864MEY = 7710 119864MEY = 9252 119864MEY = 10282 [5675 14889]119864MSY = 12385 119864MSY = 14862 119864MSY = 16517 [9374 23659]119867OA = 23128 119867OA = 27754 119867OA = 30854 [8053 53642]119867MEY = 21101 119867MEY = 25321 119867MEY = 28143 [21558 34728]119867MSY = 24607 119867MSY = 29528 119867MSY = 32895 [17670 47968]prodMEY = 476 lowast 106 prodMEY = 572 lowast 106 prodMEY = 636 lowast 106

prodMSY = 301 lowast 106 prodMSY = 361 lowast 106 prodMSY = 406 lowast 106aConfident intervals are shown in square brackets

73 Scenario 2 The differences of harvest level were 49706242 and 6320 tonnes at OA MEY and MSY level respec-tively compared to the current situation The average changein harvest level was 20 compared to the current situation

Consequences Since both carrying capacity and growth ratechange negatively the MEY and corresponding profit arefound to be lower which is expected The profit level isdecreased by about 30asMEY level compared to the presentsituation

74 Scenario 3 Compared to Scenario 0 the differences ofeconomic level were found to be BDT 138 million and BDT168million atMEY andMSY level respectively Furthermoreharvest level was changed approximately 10 compared topresent conditions

Consequences Under this scenario about 41 of change hasbeen shown at the MSY level which will not certainly be agood sign of the countryrsquos economy

75 Scenario 4 The results of the model based on thisscenario differ about 20 of effort level (at OA) and average

25 of the harvest level of OA MEY and MSY level fromscenario 0

ConsequencesThe profit impact is roughly 25 onMSY levelcompared to the reference situation

76 Scenario 5 About 35 lower harvest was accountedfor MEY under this scenario compared to present situationwhereas nearly 30 and roughly 32 change of OA andMSYlevel have been found to be occurring respectively

Consequences The profit level was approximately 56 lowerfrom the present situation

77 Scenario 6 The difference of harvest level has beenfound to be 6248 9768 and 8288 tonnes at OA MEY andMSY level respectively compared to the present scenarioIn contrast effort level has been changed only 10 from thesame situation

ConsequencesHowever profit level has been impacted at 10on MEY level compared to the current situation


78 Scenario 7 The comparative higher difference of harvestand profit level has been shown in this scenario from thereference scenario

ConsequencesThe difference of profit level has been BDT 363million at MEY level compared to the current situation

79 Scenario 8 This is the last scenario and the highestchange or difference has been observed among all thescenarios which are verymuch expected due to the significantchanges in carrying capacity and growth rate of the fisheryThe difference of harvest level has been found to be 1240014362 and 14440 tonnes at OA MEY and MSY levelrespectively compared to the current situation

Consequences This sort of dramatic change in growth andcarrying capacity has an impact on profit by 64 atMEY levelcompared to the present situation

8 Discussion

Managingmultispecies fisheries are a challenging task there-fore continuous effort has been made over the years todevelop new models to manage complex fisheries systemTo examine biological and economic over fishing of fishstocks detailed scientific data on stock levels regenerationand catch are prerequisite However less costly methods suchas observing certain indicators like catch per unit of effortchanges of price changes inmarket supplies and a percentagecomposition change of species or size over time can also begood references to address overfishing in data poor system[30] Thus traditionally CPUE had been used as an index ofstock abundance assessments [31] CPUE for fish showed anincreasing trend immediately after the 1990s and started todecline from the late 2000s which is believed to be continuedThe initial CPUE increase ismost probably due to the increaseof modernized fishing fleets in the coastal and marine waterof BOB The BOB fishery is assumed to be characterized bysmaller pelagic and smaller demersal fishery in the recentdecades [11] This could be an indication of ldquofishing downthe food webrdquo and a corresponding low CPUE Thereforeeffort pressure that is exerted on small fish which does notcontribute a lot in terms of total weight in yield consequentlytakes part in lower CPUE

The regression results showed that the GS model aimsto explain most of the variation found in the empiricaldata The results also indicated that fisheries of the BOBare characterized by increasing fishing effort and decreasingCPUE Several studies also predicted that BOB fishery couldbe unsustainable with continuous increasing of fishing effortin the absence of proper regulation and lack of implementa-tion of current initiatives [11] This is also clearly supportedby the yield-effort curve obtained from the current modelresults Present condition of high effort less harvest and lessbiomass stock also indicated that the danger of depletionof the resource cannot be ruled out [10] Fish prices havebeen rising with the declining market supplies relative to theincrease in population number and this may suggest that thestock is becoming scarce Based on the analysis of catch and

effort level of the last few years the sustainable harvest curveand catch level are expected to be coming down

To establish the ecological sustainability of current fishharvesting practices the estimatedMSY and the correspond-ing effort levels were compared with the actual catch andeffort figures MSY for the GS model of the BOB fisherywas found to be occurring very recently It is noteworthythat during the same time effort almost became doubledfrom 2003 to 2007 It has been assumed that there is littledifference between the situation in the later years and that ofOAHowever from an economic point of viewMSY does notimply efficient harvesting relating efficiency to maximizingthe net benefit from the use of economic resources thatis maximizing the resource rent [32] Therefore for theBOB fisheries management MEY is considered as a properreference point Furthermore by-catches of BOB fishery havenever been reported to be discarded by fishers Based onthe aforementioned indicators it is evident that there wasbiological overfishing but not severe for the fishery resources

A fishery cannot be sustainable if total catch exceeds theMSY level However the fact is that the MEY solution is bestcharacterized as one that considers the economic efficiencyassociated with the sustainable yield curve and there are anumber of salient benefits of pursuing such a goalmdashor at leastevaluating it for any given fishery Given this context presentmodel result showed that BOBfishery is passing a very criticaltime as both MSY and MEY have been achieved recentlyand within very short time (2003ndash2008) Most importantlyamong the reference points consideration of MEY as a keyreference point is very important due to the four major factswhich are as follows this approach is responsive to changes ineconomic conditions its implication is efficient it minimizesharvesting costs and lastly MEY might be considered tobe preferable to the MSY as a management goal is thatthe MSY solution compromises the ability of a commercialfishery to remain viable [13] The analysis on actual catchand effort figures reveals that the BOB fishery sustainedeconomic overfishing from 2005 onwards As a result evenhigher levels of effort in the later years did not get adequatequantities of catch This is obviously alarming and demandsimmediate attention of policy makers and administrationsTherefore further increase in fishing effort will certainly posea negative impact on the fish stock and none of the referencepoints (MSY and MEY) will be in equilibrium conditionIn this study [11] also commented that twice increase ofcurrent fishing effortwill severely impact the fisheries of BOBdeclining major targeted commercial pelagic and demersalfish groups Furthermore a recent study showed that most ofthe commercial fish groups of BOB had a trophic efficiency(119864119864) gt 090 indicating that the consumers are heavilyexploited by the system [11] That is why immediate attentionneeds to be taken to reevaluate the current managementmeasures for the sustainable management of BOB fishery

Sensitivity of fisheries against possible climatic changeand anthropogenic disturbances has been considered inrespect to carrying capacity growth rate and economicperformance fewer than nine different regimes A notablepercentage of change in harvest level (OA MEY and MSY)corresponding effort and profit level had been shown in


Scenarios 4 5 7 and 8 These four scenarios showed adifference between profit levels of BDT 159 million (25)BDT 262 million (41) BDT 363 (57) million and BDT408 (64) million at MEY level compared to the cur-rent situation These situations are not expected to occurin case of BOB fishery However current anthropogenicdisturbances changing climatic pattern and existing man-agement measures of BOB fishery can easily lead to asituation which is predicted under Scenarios 1 2 3 and 4However in that possible climate change consequence thefishery manager should put enough effort to keep maintainexisting MEY which in turn will help to sustain BOBfishery

Climate change may directly affect fishery productionthrough changes in reproduction growth recruitment andmigration patterns which are all affected by temperaturerainfall and hydrology [28 33] According to [34] growthmortality and recruitment parameters are extremely depen-dent on environmental conditions even between small dis-tances Therefore an assessment and projections about thefuture fishery cannot be made without due consideration ofthe climatic influences In addition the output of the surplusproduction models reveals that climate driven changes inthe productivity of the fishery can significantly influence theeconomy of fisheries An assessment report of the WorldFish Centre identified four tropical Asian countries such asBangladesh Cambodia Pakistan and Yemen as the mostvulnerable depending on the vulnerability of national eco-nomics to the impacts of climate change on fisheries [35]The database on climatic variables of tropical BOB is verypoor However average tropical sea surface temperature ispredicted to increase by 50ndash80 of the average atmosphericchange over the same period [36] This may change theaverage ocean pH and consequently can cause significantdamage to the juvenile and adults [37] and leads a shift in fish-stock distribution [2]The situationmight be the sameor evenworse for tropical BOB as the bay more often suffered fromthemultiple anthropogenic disturbances and natural disasterFinally the confidence intervals have showed how changes inthe effort levels and yields were impacted by climate changeIn current scenario 44 of effort level could increase duringconfidence interval changes at OAMEY andMSY levelsTheconfidence intervals have demonstrated that harvest mightincrease by 73 23 and 45 at OA MEY and MSY levelsrespectively

Moreover fishing zones and fish production in the coastalarea of Bangladesh are declining gradually over the years andthose attributed to the sea level rise pollution increase ofsalinity in the coastal belt frequent cyclone and changes inpH and oceanic current pattern [38] Freshwater dischargehas found to be a significant factor in the recruitment ofjuveniles and distribution of marine and estuarine speciesin the Bakkhali river estuary of Bangladesh [39] Sincea number of rivers have found their final way to BOBit is assumed that these river estuaries could play signif-icant role in recruitment of valuable commercial marinespecies Consequently under changing environmental con-ditions the natural recruitment process may face severeproblems

9 Conclusion and Recommendation

The commercial fishery of the BOB may easily lead to over-exploitation mainly attributed to the higher correspondingeffort in the absence of proper conservation managementand policymeasuresThis study also indicated that effort levelmay increase in the near future It is obvious that for MSYresource rent cannot be maximized without significant effortreduction Since in the recent years the fishery of BOB hasachieved both MSY and MEY a closer inspection is neededto ensure the sustainability of this resource exploitationAny inexpedient changes in fisheries sector that is theeffort (eg trawler fishing events and new technology) andmanagement regulation might lead the fishery into such acritical condition that could be very difficult to deal withespecially given the poor management systems and resourcesof Bangladesh The continuous increase of effort level canoccur due to the increasing population level at the coast highunemployment rate demand of fish and fishery products forthe country It is assumed that a reduction in fishing effortto attain MSY OA or MEY will raise the productivity of themarine fisheries However it will also result in the unemploy-ment of fishermen who will ease out fisheries This could bea major problem in countries like Bangladesh where coastalfishing community relies heavily onmarine fisheries or whenthere are no alternative employment opportunities outsidethe fishing sector However withdrawing fishing wouldmeanreduction in cost as well as increase in the resource rentwhich could be used to compensate the unemployed fishingpeople Additionally individual transferable quotas (ITQs)that have the potential to reduce excess competition andinvestment common in limited entry and open-access fish-eries [40] and individual quotas of habitat impact units (HIU)to mitigate possible habitat damage arising from some formsof harvesting such as bottom trawling [23] could be twogood alternatives for BOB fishery management Besides thisa campaign an awareness program and education relatedto sustainable fishing could be arranged for commercialtrawler owners A reduction of cost effort introduction andexpansion of mariculture sufficient and alternative employ-ment opportunity technical and logistic support and well-monitored market may help compensating the climate andanthropogenic driven economic loss in the fishery Finallyproper implementation of rules and regulation on differenttechnical issues of fishing should be strongly implementedand monitored

Abbreviations

BOB Bay of Bengalr Intrinsic growth rateK Carrying capacityOA Equilibriums open accessMEY Maximum economic yieldMSY Maximum sustainable yieldCPUE Catch per unit effortMFDCTG Marine Fisheries Department ChittagongBDT Bangladeshi Taka


Conflict of Interests

Authors declare that they have no competing interests

Acknowledgments

The authors like to express sincere gratitude to AssociateProfessor Arne Eide University of Tromso Norway Theauthors are also grateful to Dr Sharif Director of the MarineFisheries Department andChittagong Bangladesh for beinghelpful during the data collection

References

[1] F Fisheries ldquoThe State of World Fisheries and Aquaculture2008rdquo FAO 2009

[2] D Pauly and O Kinne ldquoGasping fish and panting squids oxy-gen temperature and the growth of water-breathing animalsrdquovol 22 International Ecology Institute 2010

[3] T K Kar and K Chakraborty ldquoA bioeconomic assessment ofthe Bangladesh shrimp fisheryrdquoWorld Journal of Modelling andSimulation vol 7 no 1 pp 58ndash69 2011

[4] D Pauly V Christensen S Guenette et al ldquoTowards sustain-ability in world fisheriesrdquo Nature vol 418 no 6898 pp 689ndash695 2002

[5] U R Sumaila W W Cheung V W Lam D Pauly andS Herrick ldquoClimate change impacts on the biophysics andeconomics of world fisheriesrdquoNature Climate Change vol 1 no9 pp 449ndash456 2011

[6] U T SrinivasanWW L Cheung RWatson andUR SumailaldquoFood security implications of global marine catch losses due tooverfishingrdquo Journal of Bioeconomics vol 12 no 3 pp 183ndash2002010

[7] B Worm E B Barbier N Beaumont et al ldquoImpacts ofbiodiversity loss on ocean ecosystem servicesrdquo Science vol 314no 5800 pp 787ndash790 2006

[8] C Mollmann R Diekmann B Muller-karulis G KornilovsM Plikshs and P Axe ldquoReorganization of a large marineecosystem due to atmospheric and anthropogenic pressure adiscontinuous regime shift in the Central Baltic Seardquo GlobalChange Biology vol 15 no 6 pp 1377ndash1393 2009

[9] DOF ldquoABrief onDepartment of Fisheries BangladeshrdquoDepart-ment of Fisheries Dhaka Bangladsh 2010

[10] M S U Khan ldquoOptimal stock harvest and effort levelof Bangladesh trawl shrimp fishery a nonlinear dynamicapproachrdquo Journal of Agriculture and Rural Development vol5 no 1 pp 143ndash149 2007

[11] M H Ullah M Rashed-Un-Nabi and M A Al-MamunldquoTrophic model of the coastal ecosystem of the Bay of Bengalusing mass balance Ecopath modelrdquo Ecological Modelling vol225 pp 82ndash94 2012

[12] M R Un-Nabi and M H Ullah ldquoEffects of Set Bagnet fisherieson the shallow coastal ecosystem of the Bay of Bengalrdquo Oceanand Coastal Management vol 67 pp 75ndash86 2012

[13] S L Larkin S Alvarez G Sylvia and M Harte PracticalConsiderations in Using Bioeconomic Modelling for RebuildingFisheries OECD Publishing 2011

[14] L G Anderson and J C Seijo Bioeconomics of FisheriesManagement John Wiley and Sons 2010

[15] CW Armstrong andU R Sumaila ldquoCannibalism and the opti-mal sharing of theNorth-EastAtlantic cod stock a bioeconomicmodelrdquo Journal of Bioeconomics vol 2 no 2 pp 99ndash115 2000

[16] C W Clark Modelling and Fisheries Management John Wileyand Sons 1985

[17] H S Gordon ldquoThe economic theory of a common-propertyresource the fisheryrdquo The Journal of Political Economy vol 62no 2 pp 124ndash142 1954

[18] S S Yoshimoto and R P Clarke ldquoComparing dynamic versionsof the Schaefer and Fox production models and their appli-cation to lobster fisheriesrdquo Canadian Journal of Fisheries andAquatic Sciences vol 50 no 1 pp 181ndash189 1993

[19] E M Thunberg and T Helser R Mayo ldquoBioeconomic analysisof alternative selection patterns in the United States Atlanticsilver hake fisheryrdquoMarine Resource Economics vol 13 pp 51ndash74 1998

[20] L G AndersonThe Economics of Fisheries Management JohnsHopkins University Press 1986

[21] C Clark Mathematical Bioeconomics The Optimal Manage-ment of Renewable Resources John Wiley and Sons New YorkNY USA 2nd edition 1990

[22] S Cunningham M R Dunn and D Withmarsh FisheriesEconomics An Introduction Mansell Publishing London UK1985

[23] D S Holland and K E Schnier ldquoModeling a rights-basedapproach for managing habitat impacts of Fisheriesrdquo NaturalResource Modeling vol 19 no 3 pp 405ndash435 2006

[24] N S Foley C W Armstrong V Kahui E Mikkelsen and SReithe ldquoA review of bioeconomic modelling of habitat-fisheriesinteractionsrdquo International Journal of Ecology vol 2012 ArticleID 861635 11 pages 2012

[25] M B Schaefer ldquoSome considerations of population dynamicsand economics in relation to the management of the commer-cial marine fisheriesrdquo Journal of the Fisheries Board of Canadavol 14 no 5 pp 669ndash681 1957

[26] O Flaaten Fisheries Economics and Management NorwegianCollege of Fishery Science University of Tromsoslash TromsoslashNorway 2011 httpmuninuitnohandle100372509

[27] M Zalewski Guidelines For the Integrated Management of theWatershed Phytotechnology and Ecohydrology UNEP Earth-print 2002

[28] A Eide and K Heen ldquoEconomic impacts of global warminga study of the fishing industry in North Norwayrdquo FisheriesResearch vol 56 no 3 pp 261ndash274 2002

[29] MFDCTG Statistics of Marine Fisheries Department MarineFisheries Department Chittagong Bangladesh 2009

[30] D Pauly ldquoTheory and practice of overfishing a SoutheastAsian perspectiverdquo in Proceedings of the Symposium on theExploitation and Management of Marine Fishery Resources inSoutheast Asia 1987

[31] A Fonteneau Atlas of Tropical Tuna Fisheries World Catchesand Environment Orstom Paris France 1997

[32] J M Hartwick and N D Olewiler The Economics of NaturalResource Use Harper and Row New York NY USA 1986

[33] A D Ficke C A Myrick and L J Hansen ldquoPotential impactsof global climate change on freshwater fisheriesrdquoReviews in FishBiology and Fisheries vol 17 no 4 pp 581ndash613 2007

[34] J M Orensanz ldquoSize environment and density regulation of ascallop stock and its management implicationsrdquo in Proceedingsof the North Pacific Workshop on Stock Assessment and Manage-ment of Invertebrates G S Jamieson and N Bourne Eds vol92 pp 195ndash227 1986


[35] E H Allison A L Perry M-C Badjeck et al ldquoVulnerabilityof national economies to the impacts of climate change onfisheriesrdquo Fish and Fisheries vol 10 no 2 pp 173ndash196 2009

[36] J M Lough ldquo10th anniversary review a changing climate forcoral reefsrdquo Journal of Environmental Monitoring vol 10 no 1pp 21ndash29 2008

[37] D J A Brown and K Sadler ldquoFish survival in acid watersrdquoAcidtoxicity and aquatic animals pp 31ndash44 1989

[38] M T H Chowdhury Z P Sukhan and H MA ldquoClimatechange and its impact on fisheries resource in Bangladeshrdquoin Procedding of International Conference on EnvironmentalAspects of Bangladesh (ICEAB rsquo10) 2010

[39] M Rashed-Un-Nabi M A-M Abdulla M U Hadayet andM G Mustafa ldquoTemporal and spatial distribution of fish andshrimp assemblage in the bakkhali river estuary of Bangladeshin relation to some water quality parametersrdquo Marine BiologyResearch vol 7 no 5 pp 436ndash452 2011

[40] D Squires S F Herrick Jr H Campbell et al ldquoIndividualtransferable quotas inmultispecies fisheriesrdquoMarine Policy vol22 no 2 pp 135ndash159 1998

Submit your manuscripts athttpwwwhindawicom

Child Development Research

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Education Research International


Biomedical EducationJournal of



Psychiatry Journal

ArchaeologyJournal of



AnthropologyJournal of


Research and TreatmentSchizophrenia


Urban Studies Research

Population ResearchInternational Journal of


CriminologyJournal of


Aging ResearchJournal of



NursingResearch and Practice

Current Gerontologyamp Geriatrics Research

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

Sleep DisordersHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

AddictionJournal of


Depression Research and TreatmentHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Geography Journal


Research and TreatmentAutism


Economics Research International


poor investment because of the lack of knowledge and infor-mation on the availability of the size of different fish stocksThough some reports are available for describing biologicaleconomical and resource management issues of the fisheriesof the Bay of Bengal (BOB) [10ndash12] a proper bioeconomicanalysis of the commercial trawl fisheries is scarce exceptfew [3 10] mostly dealing with shrimp fishery resourcesHowever a proper understanding of bioeconomic resourcesand its utilization is an urgent need to promote sustainabledevelopment ofmarine fisheries resources of BOB To controlthe stock catching and fishing effort of the fishery and to getprotection from overexploitation required strong scientificresearch in the field of fisheries biology and economics thatcan be easily examinedwith the help of a suitablemodel usingthe empirical data of the resource Bioeconomic modellinghas long been advocated as an important tool in managingsingle as well as multispecies fisheries for sustainable fisheriesmanagement

Hence the present study is undertaken in the BOBcommercial trawl fishery to assess the sustainability ofmarinefish production and to suggest appropriate policy recom-mendations for improving the capture fisheries scenario ofthe country This study also puts a little effort to analyzethe potential impacts of climate change and anthropogenicdisturbances (eg pollution habitat degradation errors ofestimation due to lack of accurate information and otherunpredictable events) on the fish stock resources To do sothis paper has made some assumptions about climate changeand anthropogenic effects on r (intrinsic growth rate) and K(carrying capacity) in a surplus production model

2 Theoretical Model

Bioeconomic theory in fisheries combines the biologicaland economic aspects of a fishery to explain stock catchand effort dynamics under different regimes and providesinsights into the optimal management of the stock [13]The bioeconomic model provides an integrated approach forevaluation of effective fishery management strategies [14ndash16]

3 Reference Equilibriumsand Management Regimes

The overall goal of fisheries management is to providesustainable biological social and economic benefits fromrenewable aquatic resources For the long-term sustainabilityand for enhancing the revenue of the fishery static as well asthe dynamic behavior of the system should be investigatedby achieving the targeted reference points Maximum eco-nomic yield (MEY) and maximum sustainable yield (MSY)represent different fisheries objectives which are the basisof identifying suitable management measures The otherreference point namely equilibriums open access (OA) ismore likely a regime rather than a performance objective(such as MSY and MEY) Open access represents a lackof property rights to restrict harvesters from common poolresources However OA is not socially efficient (suboptimal)because of its higher effort [17] Moreover this practice can

Tota

l cos

tTo

tal r

even

ue

MSY Total cost

MEYOAY

Total revenue

Fishing effortEMEY EMSY EOAY

Figure 1 The Gordon-Schaefer model

also produce a lower level of catch as compared to the MSYat comparably higher cost Therefore MEY is considered asthe optimal solution since it equates the marginal revenue ofan additional unit of effort However the ldquooptimalrdquo allocationof fishery resources can be determined by bioeconomicmodelling comparingOAMSY andMEY solutions and oftendepends on the objective of particular fisheries management

4 Modelrsquos Choice and Description

In bioeconomic modelling surplus production model whichis an equilibrium model has the capability to determine thesustainable yield from a given fishing effort (Figure 1) andregarded as valuable tool for its first approximation evenin time or data limiting condition [18] These models aregenerally used to examine economic performance or rentdissipation in a fishery [19] and well known in the fisherieseconomics literature [20ndash22] Moreover it is simple and easyto incorporate environmental attributes in the model andits parameters can be estimated using catch and effort dataThe Gordon-Schaefer (GS) model with extension (such ashabitat environmental variables) can potentially identifythe underlying relationship between incorporated variablesstock effort and harvest under open access and maximumeconomic yield managed fisheries [23 24]

The GS model originated from Gordon [17] and Schaefer[25] Therefore the GS surplus production model has beenselected for this study The model has a big advantage ofrequiring limited data and could produce rough guidance onfleet size in the case of single-species as well as multispeciesfishery

Fisheries based on highly productive biological resourceswith large r (intrinsic growth rate) and K (carrying capacity)may sustain a large fishing effort under OA [26] In allpopulations natural surplus growth is small for both highand low stock level and the largest for some intermediatelevel However the GS model is based on the logistic growthequation

119865 (119883) = 119903119883(1 minus119883

119870) (1)




119867(119864119883) = 119902119864119883 (2)


119889119909

119889119905= 119865 (119883) minus 119867 (119864119883) = 0 (3)


119883 = 119870(1 minus119902119864

119903) (4)


119867(119864) = 119902119870119864(1 minus119902119864

119903) = 119902119870119864 minus

11990221198701198642

119903 (5)


CPUE = 119867119864= 119902119870 minus

1199022119870119864

119903 (6)


TR (119864) = 119901 sdot 119867 (119864) (7)


TC (119864) = 119888 sdot 119864 (8)



prod(119864) = TR (119864) minus TC (119864) (9)










(ie r-25 and K-25)





119901119867

119864= 119888

119867

119864=119888

119901

(10)




Catch(tonnes)

Effort(days) CPUE

1985-1986 14 5500 1783 30851986-1987 18 4769 2351 20281987-1988 19 4393 2331 18851988-1989 8 931 617 15091989-1990 8 2105 990 21261990-1991 12 1532 721 21251991-1992 14 1974 1421 13891992-1993 12 2545 1545 16471993-1994 11 3305 1228 26911994-1995 14 4404 1354 32531995-1996 12 4568 1432 3191996-1997 14 5793 1656 34981997-1998 13 7515 1856 40491998-1999 18 6680 2136 31271999-2000 21 8017 2517 31852000-2001 31 16027 3871 4142001-2002 36 16586 4841 34262002-2003 42 19428 5414 35882003-2004 49 23207 6284 36932004-2005 68 25895 8535 30342005-2006 78 27096 11469 23632006-2007 88 29446 11462 25692007-2008 95 29176 13368 2183


119862 (119883) =TC (119864)119867=119888119864

119902119864119883=119888

119902119883 (11)



119887 (119883) = 119901 minus119888

119902119883 (12)




119883infin=119888

119901119902 (13)


119867(119864) = 119886119864 + 1198871198642 (14)


CPUE = 119886 + 119887119864 (15)




119864MSY = (minus119886

2119887) (16)


MSY = (minus1198862

4119887) (17)


MC = AR (18)

or

119888 =119901119867 (119864)

119864

119888119864 = 119901119867 (119864) equiv 119864OAY = (119888119901 minus 119886

119887)

(19)


119864MEY = (119888119901 minus 119886

2119887) (20)






0 5000 20000 25000 3000010000 150000

5

10

15

20

25

30


Yiel

d in

thou

sand

tonn

es




119870 =119886

119902 (21)

119903 =minus119886119902

119887 (22)

7 Results



119867(119864) = 3974119864 + (minus000012031198642) (23)


































8 Discussion













Abbreviations





Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal








119867(119864119883) = 119902119864119883 (2)


119889119909

119889119905= 119865 (119883) minus 119867 (119864119883) = 0 (3)


119883 = 119870(1 minus119902119864

119903) (4)


119867(119864) = 119902119870119864(1 minus119902119864

119903) = 119902119870119864 minus

11990221198701198642

119903 (5)


CPUE = 119867119864= 119902119870 minus

1199022119870119864

119903 (6)


TR (119864) = 119901 sdot 119867 (119864) (7)


TC (119864) = 119888 sdot 119864 (8)



prod(119864) = TR (119864) minus TC (119864) (9)










(ie r-25 and K-25)





119901119867

119864= 119888

119867

119864=119888

119901

(10)




Catch(tonnes)

Effort(days) CPUE

1985-1986 14 5500 1783 30851986-1987 18 4769 2351 20281987-1988 19 4393 2331 18851988-1989 8 931 617 15091989-1990 8 2105 990 21261990-1991 12 1532 721 21251991-1992 14 1974 1421 13891992-1993 12 2545 1545 16471993-1994 11 3305 1228 26911994-1995 14 4404 1354 32531995-1996 12 4568 1432 3191996-1997 14 5793 1656 34981997-1998 13 7515 1856 40491998-1999 18 6680 2136 31271999-2000 21 8017 2517 31852000-2001 31 16027 3871 4142001-2002 36 16586 4841 34262002-2003 42 19428 5414 35882003-2004 49 23207 6284 36932004-2005 68 25895 8535 30342005-2006 78 27096 11469 23632006-2007 88 29446 11462 25692007-2008 95 29176 13368 2183


119862 (119883) =TC (119864)119867=119888119864

119902119864119883=119888

119902119883 (11)



119887 (119883) = 119901 minus119888

119902119883 (12)




119883infin=119888

119901119902 (13)


119867(119864) = 119886119864 + 1198871198642 (14)


CPUE = 119886 + 119887119864 (15)




119864MSY = (minus119886

2119887) (16)


MSY = (minus1198862

4119887) (17)


MC = AR (18)

or

119888 =119901119867 (119864)

119864

119888119864 = 119901119867 (119864) equiv 119864OAY = (119888119901 minus 119886

119887)

(19)


119864MEY = (119888119901 minus 119886

2119887) (20)






0 5000 20000 25000 3000010000 150000

5

10

15

20

25

30


Yiel

d in

thou

sand

tonn

es




119870 =119886

119902 (21)

119903 =minus119886119902

119887 (22)

7 Results



119867(119864) = 3974119864 + (minus000012031198642) (23)


































8 Discussion













Abbreviations





Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal








Catch(tonnes)

Effort(days) CPUE

1985-1986 14 5500 1783 30851986-1987 18 4769 2351 20281987-1988 19 4393 2331 18851988-1989 8 931 617 15091989-1990 8 2105 990 21261990-1991 12 1532 721 21251991-1992 14 1974 1421 13891992-1993 12 2545 1545 16471993-1994 11 3305 1228 26911994-1995 14 4404 1354 32531995-1996 12 4568 1432 3191996-1997 14 5793 1656 34981997-1998 13 7515 1856 40491998-1999 18 6680 2136 31271999-2000 21 8017 2517 31852000-2001 31 16027 3871 4142001-2002 36 16586 4841 34262002-2003 42 19428 5414 35882003-2004 49 23207 6284 36932004-2005 68 25895 8535 30342005-2006 78 27096 11469 23632006-2007 88 29446 11462 25692007-2008 95 29176 13368 2183


119862 (119883) =TC (119864)119867=119888119864

119902119864119883=119888

119902119883 (11)



119887 (119883) = 119901 minus119888

119902119883 (12)




119883infin=119888

119901119902 (13)


119867(119864) = 119886119864 + 1198871198642 (14)


CPUE = 119886 + 119887119864 (15)




119864MSY = (minus119886

2119887) (16)


MSY = (minus1198862

4119887) (17)


MC = AR (18)

or

119888 =119901119867 (119864)

119864

119888119864 = 119901119867 (119864) equiv 119864OAY = (119888119901 minus 119886

119887)

(19)


119864MEY = (119888119901 minus 119886

2119887) (20)






0 5000 20000 25000 3000010000 150000

5

10

15

20

25

30


Yiel

d in

thou

sand

tonn

es




119870 =119886

119902 (21)

119903 =minus119886119902

119887 (22)

7 Results



119867(119864) = 3974119864 + (minus000012031198642) (23)


































8 Discussion













Abbreviations





Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal








0 5000 20000 25000 3000010000 150000

5

10

15

20

25

30


Yiel

d in

thou

sand

tonn

es




119870 =119886

119902 (21)

119903 =minus119886119902

119887 (22)

7 Results



119867(119864) = 3974119864 + (minus000012031198642) (23)


































8 Discussion













Abbreviations





Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal
































8 Discussion













Abbreviations





Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal











Abbreviations





Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal








Acknowledgments


References


















































Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal




















Psychiatry Journal



















Volume 2014


AddictionJournal of




Geography Journal





Research Article Bioeconomics of Commercial Marine...

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