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This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies areencouraged to visit:
journa l homepage: www.e lsev ier .com/ locate / f i shres
Assessing the stocks of the primary snappers caught in Northeastern BrazilianReef Systems. 2-A multi-fleet age-structured approach
Thierry Frédoua,b,∗, Beatrice P. Ferreirab, Yves Letourneura
a Université de la Méditerranée, Centre d’Océanologie de Marseille, UMR CNRS 6540, 13288 Marseille cedex 9, Franceb Departamento de Oceanografia, Universidade Federal de Pernambuco, Recife, Pernambuco 50739-540, Brazil
a r t i c l e i n f o
Article history:Received 23 October 2008Received in revised form 29 April 2009Accepted 5 May 2009
This study investigates the exploitation of Lutjanus analis, Lutjanus chrysurus, Lutjanus jocu and Lutjanussynagris from multiple gear types in Northeast Brazil using models allowing for technological interactions.The spatial distribution of species and fleets have played an important role in the Northeastern Brazilianartisanal fishery. The results of a predictive model by Thompson and Bell, along with a catch-at-agemodel, clearly showed that different fleets had distinct effects on snapper stocks. The fishery mortality(F) from small-scale fleets (‘Jangada’ and ‘Paquete’) was higher for coastal species such as L. synagris thatare typically found in these areas. Conversely, L. jocu, that inhabits deeper waters, was most affectedby motorised and sailing boats. Snappers were exploited differently at each of their life history stages.‘Paquetes’ mainly exploited juveniles, whereas boats caught larger individuals farther from the coast. Asa consequence, effort control through fleet regulation seems to be much more applicable in a tropicalcontext than traditional catch control. Further modelling studies and management options relevant tothe Northeast Brazil fishery are discussed.
Reef fish communities form the basis of artisanal fisheries andhave an important socio-economic role in Northeast Brazil (Ferreiraand Maida, 2001). Large carnivores such as groupers and snap-pers are among the most heavily exploited reef fishes in tropicalseas (Jennings and Polunin, 1997). The analysis of the catch fromartisanal reef fisheries confirmed that snappers were the mainresources driving fishing activities (Frédou et al. 2006). Despitetheir economic importance, the status of the exploited snapperpopulations off of Northeast Brazil is poorly documented. In thecompanion paper (Frédou et al., 2009), traditional stock assess-ment models were used to describe the current status of the fivemain species of snapper, Lutjanus analis, Lutjanus chrysurus, Lut-janus jocu, Lutjanus synagris and Lutjanus vivanus, exploited offof Northeast Brazil. These stocks were fully or over-exploited.The single-species models used were yield-per-recruit and Vir-tual Population Analysis—VPA (Sparre and Venema, 1998), whichwas further divided into three variants: length cohort analysis,pseudo-cohort VPA, and real-cohort VPA, along with the predictiveThompson and Bell model (Thompson and Bell, 1934).
∗ Corresponding author. Present address: Centro de Geociências, UniversidadeFederal do Pará, Campus Universitário do Guamá, Belém, Pará 66075-110, Brazil.
Most tropical fisheries support a large number of co-existingfleets and gear whose biological impacts vary (Jennings et al., 2001).Models for the management of single-species fished using a singletype of gear may be inadequate for most tropical fisheries and arelikely ineffective for predicting change at the assemblage level.
Multi-species fisheries occur due for biological, technical andeconomic reasons (Magnusson, 1995). Technical interactions mayarise through the incidental catch of non-target species (by-catch)in targeted fisheries, by the exploitation of a target group of speciesby the same gear and, primarily, by the co-existence of differ-ent fleets exploiting the same resource and/or the same fishinggrounds. These technical interactions caused by different fishinggear exploiting the same resource are simpler to study and canprovide useful results that can be applied in stock assessment andmanagement (Pikitch, 1988).
During their investigation, Frédou et al. (2006) showed that themain reef species exploited by the artisanal fishery in NortheastBrazilian were five species of snapper. Frédou et al. (2006) andFrédou and Ferreira (2005) argued that the type of gear used was notthe primary technological factor but that there was a clear effect ofthe fleet category. Therefore, models that consider multi-fleet inter-actions rather than multi-gear interactions were chosen. Modelsconsidering multi-fleet interactions were also preferable for man-agement reasons, as most fishers use various gear simultaneously,which makes the enforcement of multi-gear management decisionsimpractical. The different model outputs, their limitations, and
98 T. Frédou et al. / Fisheries Research 99 (2009) 97–105
their advantages are discussed. Ultimately, we compare these moresophisticated approaches with traditional single-species modelsthat are familiar to fisheries biologists and are described and anal-ysed in the companion paper (Frédou et al., 2009).
2. Materials and methods
Data were collected for five years, from 1996 to 2000, withinthe research program REVIZEE (Living Resources in the BrazilianExclusive Economic Zone(MMA, 2006). Routine visits to the landingsites of the commercial fishery were undertaken along the North-east Brazilian coast (five federal states: Ceará, Rio Grande do Norte,Pernambuco, Alagoas, Bahia), and interviews were conducted withskippers and managers (for details see Frédou et al., 2009). Weconsidered four out of the five Lutjanus species (L. analis, L. jocu,L. chrysurus, L. synagris) that represent most of the catch artisanalfishery catch in that area (Frédou and Ferreira, 2005; Frédou et al.,2006). The fifth specie, L. vivanus, was disregarded as we did nothave adequate information to feed the model.
The reef fishery can be divided into four categories of fleets asfollows, listed from the most rudimental to the most technicallyadvanced fleet: “Paquete” (PQT), “Jangada” (JAN), “Sailing boat”(BOV), and “Motorised boat” (BOM; see Table 1 for details). Eachfleet category exploits a particular combination of species and aparticular size range. The fleet dynamics of Northeast Brazil aretechnologically heterogeneous and determine the catch composi-tion (Frédou and Ferreira, 2005).
Two models with different data input and perspectives wereused: (a) the Thompson and Bell (1934) predictive model, whichincluded the effects of different fleets exploiting the same resource;and (b) the multi-fleet catch-at-age model (Fournier, 1996).
2.1. Thompson and Bell’s predictive model
The Thompson and Bell (TB) model predicts the effect of changesin the fishing pattern, and in this study considered multi-fleetexploitation. The analyses within the model are very different fromthose in Virtual Population Analysis (VPA) and cohort analysis. Themain input is the F-at-age array that is obtained using VPA (Frédouet al., 2009). New values of F can be obtained by multiplying thereference F-array as a whole by a certain factor, usually called X.The total yield and fishing mortality were separated into fleet com-ponents using the exponential decay model and the catch equation(Sparre and Venema, 1998).
Initially, the same factors, XBOM = XBOV = XPQT = XJAN, were appliedto the F-values for each fleet. By carrying out various calculationswith different values for X (F-factors), graphs were drawn to pre-dict the effects of changes in fishing mortality on the yield and theaverage biomass. In this study, four scenarios, detailed in Table 2,were chosen considering that the reef fishery dynamic is driven bythe different fleet categories exploiting the resources (Frédou et al.,2006).
Table 1Description of fleet categories.
Fleet type Code Depth range (m)25%–median–75%
Description
Paquete PQT 10–15–25 Sail, wooden, flat deck, withoutkeel or cabin Size < 6 m, nostorage capacity
Jangada JAN 15–30–40 Sail, wooden, flat deck, withoutkeel or cabin, storage capacity(insulated box)
Bote à vela BOV 40–75–95 Sail, cabin, size < 15 m, storagecapacity (on ice)
Table 2Thompson and Bell model projections according to the harvest rate of the fleets.
Scenario 1 Changes in harvest rate for ‘paquete’ (PQT) and ‘jangada’ (JAN):XBOM and XBOV remain constant. XPQT and XJAN were allowed tovary.
Scenario 2 Changes in harvest rate for ‘motorised boat’ (BOM) and ‘sailingboat’ (BOV): XPQT and XJAN were kept constant whereas XBOM andXBOV were allowed to vary.
Scenario 3 Changes in harvest rate for ‘motorised boats’: XBOV, XPQT and XJAN
were kept constant and XBOM was allowed to vary.Scenario 4 Changes in harvest rate for ‘paquete’: XBOV, XBOM and XJAN were
kept constant whereas XPQT was allowed to vary. For L. chrysurusand L. synagris only. L. analis and L. jocu was not caught by ‘paquete’.Changes in harvest rate for ‘Sailing boat’ XJAN and XBOM were keptconstant whereas XBOV varied. For L. analis and L. jocu only
2.2. Multi-fleet catch-at-age model
In the proposed model, exploitation was fleet-specific in orderto consider the effects of the different fleets on the stock structure ofeach species of Lutjanus analysed. The catch-at-age model requiresdata on numbers-at-age and fishing effort by gear and by year, andmakes an assumption about natural mortality.
The catch-at-age model was implemented in the program ‘ADModel Builder’ (Fournier, 1996), which is a tool for the rapid devel-opment and implementation of non-linear statistical models.
The instantaneous fishing mortality rate by fleet, year and age,Ff(ij), is given by:
Ff(ij) = qfEg(i)Sf (j) exp{ϕf(i)}, (1)
where i indicates the fishing year, j the index age class and f the fleet.qf denotes catchability by gear and Sg(j) denotes the selectivity coef-ficients by gear and age. Ef(i) is the observed fishing effort by gearin year i and ϕf(i) is the deviation from the expected relationshipbetween the observed fishing effort and the resulting fishing mor-tality in year i. Specifically, FPQT(ij), FJAN(ij), FBOV(ij) and FBOM(ij) denotethe fishery mortality by year and age for ‘paquete’ (PQT), ‘jangada’(JAN), ‘sailing boat’ (BOV) and ‘motorised boat’ (BOM) exploitation,respectively. Total fishery mortality is then given by the sum:
Fij = FPQT(ij) + FJAN + FBOV(ij) + FBOM(ij) (2)
The model considered each of the different fleets for eachspecies. For L. analis and L. jocu, only JAN, BOV and BOM significantlyexploited these species. For L. synagris and L. chrysurus, ‘paquetes’were also included in the analysis. Furthermore, it is assumed thatthe natural mortality rate (M) is known (see Frédou et al., 2009 forthe natural mortality assumed for each species)Total mortality (Z)is given by:
Z(ij) = F(ij) + M. (3)
An iterative estimation approach was used to estimate param-eters such as catchability and selectivity. The iterative schemerequires initial estimates of population scale and relative pop-ulation (denoted ‘relpop’) but these are subsequently updatediteratively. For all years, estimates of recruits (at age 1) andnumbers-at-age of all ages for the first year of analysis are given by:log (initial population) = log (relpop) + log (population scale) where:
N(i1) = exp{log(initial population(i))}; and (4)
N(i1) = exp{log(initial population(j))} (5)
Providing estimates of the initial population in year 1, N 1,1, N1,2. . ., N1,j and the knowledge of recruitment in each year (N1,1,N2,1,. . .), the seasonal exponential decay equation yields:
T. Frédou et al. / Fisheries Research 99 (2009) 97–105 99
where Sv(i,j), the survival by year and age class, is Sv(i,j) = exp−Z.Thebiomass (B) at the beginning of the year (1st of January) is calculatedthrough the equation:
Bi =∑
WjN(ij), (7)
where Wj is the weight-at-age.The predicted catch by each fleet, year i and age j, (CPQT(i,j), CJAN(i,j)
CBOV(i,j) and CBOM(i,j), respectively) is given by:
CBOM(ij) = (FBOM(ij)/Z(ij))[1 − Sv]N(i,j) and (8)
CBOV(ij) = (FBOV(ij)/Z(ij))[1 − Sv]N(i,j) (9)
A statistical model was constructed with two elements, catchand effort, from which parameter estimates were derived. Thetrue catches CPQT(i,j), CJAN(i,j),CBOV(i,j) and CBOM(i,j) for each yearand for each fleet were not known but estimates of CoPQT(i,j),CoJAN(i,j), CoBOV(i,j) and CoBOM(i,j) (observed catches—input catch-at-age matrix by fleet), respectively, were recorded.
Parameter estimation was based on maximum likelihood (L),with catches assumed to follow a normal distribution and likeli-hood functions given by:
L1 =I∏
i=1
J∏j=1
(2��2)
−1/2
exp(−(CoBOM − CBOM(i,j))2/CBOM()
+I∏
i=1
(2��2)
−1/2
exp(−12
(ϕl(i)2)) (10)
L2 =I∏
i=1
J∏j=1
(2��2)
−1/2
exp(−(CoBOV − CBOV(i,j))2/CBOV()
+I∏
i=1
(2��2)
−1/2
exp(
−12
(ϕ1(i)2)
)(11)
L3 =I∏
i=1
J∏j=1
(2��2)
−1/2
exp(−(CoJAN − CJAN(i,j))2/CJAN()
+I∏
i=1
(2��2)
−1/2
exp(
−12
(ϕl(i)2)
); and (12)
L4 =I∏
i=1
J∏j=1
(2��2)
−1/2
exp(−(CoPQT − CPQT(i,j))2/CPQT()
+I∏
i=1
(2��2)
−1/2
exp(
−12
(ϕl(i)2)
)(13)
The parameters of the model were estimated by maximising thesum of the independent log-likelihoods: log L1 + log L2 + log L3 + logL4. Note that only three log-likelihoods (L1, L2 and L3) were calcu-lated for L. analis and L. jocu as the fleet PQT was not relevant to theexploitation of these species (Frédou et al., 2006).
3. Results
3.1. Thompson and Bell model simulations
The F value for Lutjanus analis at the maximum sustainable yield(Fmax) is set at Fcurrent, and predictions show that motorised boats(BOM) mostly influenced the yield curve since this fleet is responsi-ble for the majority of the catch of this species. The stock was foundto be fully exploited by the BOM fishery, whereas BOV did not reacha maximum, and JAN Fmax was 30% below Fcurrent (Fig. 1A). Consid-ering the scenarios, when the ‘jangada’ (JAN) or the sailing boat(BOV) fishery increased and the others remained constant (i.e., sce-narios 1 and 4), the yield did not change within the range of theX-factors considered (Fig. 1B). Scenario 2 showed a maximum yieldat the current fishing mortality (F). However, in the scenario 3, theFmax for BOM was a little beyond the current F (Fig. 1B).
The F value for L. jocu at maximum yield was 20 % higher thanthe current F (Fig. 2A). The Fmax for JAN and BOM were 20% and 40%superior to the current F (X-factor = 1), respectively, whereas theoptimum F for BOV was 10% lower (Fig. 2A). The effect of changes inthe ‘jangada’ (JAN) fishery, while the boat fishery (BOM, BOV) waskept at a constant level (scenario 1), was relatively low, indicatingthat this fleet does not have a large influence on the yield of thisspecies (Fig. 2B). Considering scenarios 2–4, it was clear that onlythe BOM fishery had a significant impact on the total yield.
The total yield of L. chrysurus did not have a maximum withina range of 0–3 in the factor X (Fig. 3A). The Fmax for BOV and BOMwere 10% and 60% higher than the current F, respectively. As alreadyreported for traditional modelling approaches (Frédou et al., 2009),the maximum yield was not very discernible for any scenario.However, as the yield curve presented an asymptotic shape, theincrease in factor X from one variable did not relevantly change theyield. This indicates that if exploitation increases over that whichis currently reported, the species would be considered to be fullyexploited. Considering scenarios 1–4, no maximum level of factor Xwas defined within the range considered (Fig. 3B). The combinationof scenarios investigated highlighted the influence of the BOM fleetwithin the exploitation of the species.
Similarly to L. chrysurus, the effect of change for each fleetshowed that the yield curve presented an asymptotic shape, andno maximum value was discernible for any scenario of L. synagris(Fig. 4A). The Fmax value was 140% higher than the Fcurrent value, butas for L. chrysurus, the yield curve presented an asymptotic shape,and the increase in factor X from a value of one did not substantiallychange the yield. Thus, this species could be already considered tobe fully exploited. JAN and PQT clearly played an important role inthe exploitation of this species.
3.2. Multi-fleet catch-at-age model
The multi-fleet catch-at-age optimisation (maximisation of thelikelihood) algorithm did not converge to an optimum solution forL. analis. Then no further development of this model for this specieswas continued.
For L. jocu, BOM (motorised boats) was the dominant modeof exploitation in Northeast Brazil during the period 1996–2001(Fig. 5A). Age-classes 4–10 were exposed to the highest fishingmortality. The total fishing mortality (for the weighted mean, F)was 0.05 (Fig. 5A), and the exploitation rate (E; E = F/Z) was 0.30.Biomass decreased from approximately 70–10000 tonnes over thestudy period (Fig. 5B). Catchability of BOV was lower than that ofBOM and JAN (Table 3).
For L. synagris, JAN and PQT were the dominant modes ofexploitation in Northeast Brazil during the period 1996–2001(Fig. 6A). Individuals older than 12 years were exposed to the high-est fishing mortality. Total fishing mortality (the weighted mean, F)
100 T. Frédou et al. / Fisheries Research 99 (2009) 97–105
Fig. 1. Total yield per 1000 recruits of L. analis separated by fleet components. (A) Situation where the X-factors varied equally; and (B) scenarios where X-factors were allowedto vary in each fleet (see text for details). Dashed line: BOM; dotted line: JAN; dashed and circle line: BOV; and continuous line: total yield. Full black dots represent the Fmax
for each fleet.
was 0.16 (Fig. 6B), and E was 0.53. Biomass decreased slightly from13 to 12 thousands of tonnes during the study period (Fig. 6B). Thehighest catchability reported was that of PQT (Table 3).
For L. chrysurus, JAN was the dominant mode of exploitation forthe youngest age-classes (3–9), and BOV dominated after the age of11 during the period 1996–2001 (Fig. 7A). Individuals from all ageclasses were exposed to similar fishing mortality, although fish-ing mortality increased slightly after 10 years. The mean weightfor total fishing mortality was 0.14, and E was 0.45. For the wholeperiod, biomass decreased from approximately 12–3.3 thousand
tonnes (Fig. 7B). Catchability of BOV was higher than that reportedfor the other fleets (Table 3).
4. Discussion
In this study, results of the Thompson and Bell model suggestthat some fleet components already reached or exceeded their opti-mum fishing mortality level (Frédou et al., 2009). The multi-fleetcharacteristic of the reef fishery in Northeast Brazil is clear when thefishery mortality is analysed by fleet. The assessment by fleet fol-
T. Frédou et al. / Fisheries Research 99 (2009) 97–105 101
Fig. 2. Total yield per 1000 recruits of L. jocu separated by fleet components. (A) Situation where the X-factors varied equally; and (B) Scenarios where X-factors were allowedto vary in each fleet (see text for details). Dashed line: BOM; dotted line: JAN; dashed and circle line: BOV; and continuous line: total yield. Full black dots represent the Fmax
for each fleet.
lowed the pattern observed in a previous study (Frédou et al., 2006).‘Paquete’ and ‘jangada’ fleets primarily affected the L. synagris stockand, to a lesser extent, the L. chrysurus stock, whereas ‘boats’, partic-ularly BOM, were the main fleet that exploited L. analis and L. jocu.This outcome was expected, as previously described by Sparre andVenema (1998); i.e., the higher the effort of the most relevant fleet(in this case, BOM), the smaller the share that is left for the fleetsthat are responsible for the lower catches. As a result, the yieldchanged little in the scenarios in which boats remained constant(scenarios 1 and 4). An opposite pattern was observed in scenarios2 and 3. This pattern indicates that fisheries could be controlled bya fleet regulation.
As in the Thompson and Bell model results, the catch-at-agemodel showed relatively high exploitation rates and a decrease inbiomass during the study period, suggesting over-exploitation orat least full exploitation. Conversely, the patterns found for L. jocuin the catch-at-age and Thompson and Bell models were different.According to the catch-at-age model, the stock was underexploited,where the Thompson and Bell model reported that the stock wasfully exploited. The main fishing effects of ‘jangada’ and ‘paquete’were reported for typical shallow waters species, namely L. synagrisand L. chrysurus (Frédou and Ferreira, 2005). The ‘boats’ fleets (BOM,BOV) primarily affected L. jocu. The catch-at-age model suggeststhat members of a cohort may have differential vulnerability to the
102 T. Frédou et al. / Fisheries Research 99 (2009) 97–105
Fig. 3. Total yield per 1000 recruits of L. chrysurus separated by fleet components. (A) Situation where the X-factors varied equally; and (B) Scenarios where X-factors wereallowed to vary in each fleet (see text for details on the scenarios). Dashed line: BOM; dotted line: JAN; dashed and circle line: BOV; and continuous line: total yield. Full blackdots represent the Fmax for each fleet.
fleet depending on fish size. For example, young L. jocu (small indi-viduals) that live in shallower waters were preferentially affectedby ‘paquete’ and ‘jangada’ (Frédou and Ferreira, 2005). L. chrysuruswas exposed to similar fishing mortality in all age classes, whereasthe oldest specimens of L. synagris were more affected. Total fish-ing mortality obtained with VPA analysis was very high (nearly 1)when computed with the catch-at-age model. Frédou et al. (2009)reported an F value equivalent to 0.24, 0.27 and 0.19 for L. jocu,L. chrysurus and L. synagris, respectively. Biomass values were alsowithin a similar range for both approaches. However, the advan-tage of the catch-at-age model applied here is that it may predict
catches considering that the exploitation is fleet-specific and thatit may highlight the effects of the different boat categories on thestock structure. The computational program is a powerful tool inestimating non-linear equations that contain ‘free parameters’ (ascatchability and error coefficients). Regrettably, the catch-at-agemodel did not find a satisfactory solution in estimating the param-eters. This may be due to the short time–series, which shows avery low contrast, classified as a one-way trip fishery making modelconvergence difficult (Hilborn and Walters, 1992).
An interesting point, which is somehow reassuring, is that theoutputs of the different models were similar to one to another
T. Frédou et al. / Fisheries Research 99 (2009) 97–105 103
Fig. 4. Total yield per 1000 recruits of L. synagris separated by fleet components. (A) Situation where the X-factors varied equally; and (B) Scenarios where X-factors wereallowed to vary in each fleet (see text for detail on scenarios). Dashed line: BOM; dotted line: JAN; dashed and circle line: BOV; and continuous line: total yield. Full blackdots represent the Fmax for each fleet.
Table 3Results from multi-fleet catch-at-age model. Logarithm of the catchability (q) of the specie (and standard deviation) during the period 1996–2001. PQT = ’Paquete’;JAN = ‘Jangada’; BOV = Sailing boat; and BOM = Motorised boats.
104 T. Frédou et al. / Fisheries Research 99 (2009) 97–105
Fig. 5. Statistical catch-at-age model results for L. jocu. (A) Fishing mortality cat-egorised by fleet for all age classes. Key: �: JAN; �: BOV; �: BOM; : Total. (B)Biomass profile during the study period.
Fig. 6. Statistical catch-at-age model results for L. synagris. (A) Fishing mortalitycategorised by fleet for all age classes. Key: �: JAN; �: BOV; �: BOM; Total. (B)Biomass profile during the study period.
Fig. 7. Statistical catch-at-age model results for L. chrysurus. (A) Fishing mortalitycategorised by fleet for all age classes. Key: �: JAN; �: BOV; �: BOM; Total. (B)Biomass profile during the study period.
despite the different strengths and drawbacks of each model. Over-all, this study follows the conclusion of the published studies onreef fisheries, which warned that fishing levels were beyond sus-tainable levels or at least beyond any conservative reference point(Ault et al., 2001, 2002; Grandcourt et al., 2008; Mendoza and Larez,2003; Reef Fish Stock Assessment Panel, 1999).
There is an increasing number of fishing resource manage-ment models that consider factors other than those directly relatedto fish biology and attempt to comprehend the ecosystem as awhole (Pauly et al., 2002). However, multi-species catch-at-agemodels, biomass models and environment-based models (e.g., Eco-path, Ecosim) require a great deal of data, are complex and aresources of misconceptions in assumptions and theories (Cotteret al., 2004). Uncertainty and a poor knowledge of the resourcesare major setbacks for the development of a suitable assessmentand management of fish stocks in developing countries (Lucena,2000).
In this study, models with fewer data requirements were pre-ferred. Catch-at-age models that incorporate characteristics of themulti-fleet exploitation improved the assessment outputs andallowed a better insight for management options. Fleet interac-tions have important implications for fish stock management. Fleetsin Northeast Brazil operate in different ways and therefore affectstocks differently as they come into contact with stocks of differentspecies and sizes that develop different fishing patterns. Braziliansnappers, like most fish populations (Walters and Martell, 2004),exhibit a very complex spatial organisation with a spatial segre-gation of fish of different sizes or ages that affects the catchesand the way fishers exploit this spatial organisation. In their study,Frédou et al. (2006) found that each vessel has a catchability andefficiency specific to its design and to its operational range in rela-tion to the species targeted and that similar fleets may have adistinct impact consistent with the fishing area in which they oper-ate. Effort control through fleet regulation seems to be much moreapplicable in a tropical context than traditional catch control whereeffective monitoring and surveillance schemes are hardly applica-ble.
Although it is clear that technological interactions improvedthe assessment, it is now generally accepted that managementstrategies should be defined in terms of biological, economic,social, and, consequently, political objectives. Indeed, economic andsocial equilibrium will not occur while a stock is below a criticalsize, which threatens the long-term sustainability of the fishery.However, biological objectives are unlikely to be met without con-sideration on economic and social aspects. New approaches areurgently needed to provide a sound basis for management adviceon reducing fishing effort levels. Innovative approaches in Brazilare based on the Rapid fisheries appraisal, Rapfish (Pitcher andPreikshot, 2001), which compiled ecological, technological, soci-ological and economic information and revealed new insights thattraditional stock assessments would not have detected (Isaac et al.,2009; Lessa et al., 2009).
The Brazilian management process is, traditionally, controlledtop–down with very limited participation of fisher communities.The low involvement of this key stakeholder will make it difficultto motivate the community to support and adhere to conservativemanagement strategies, which should be an essential issue in acompetitive tropical system with a weak enforcement capacity. Thespatial component, in terms of biological and technological factors,is the major element that affects the dynamics of the reef fishery inNortheast Brazil. There is a variety of management options for thereef fishery for selectively protecting and exploiting fish (Waltersand Martell, 2004), although few apply in Brazil. The implemen-tation of rights-based management, such as territorial use rights,can lead to an efficient control, motivating local communities andpromoting ecological awareness.
T. Frédou et al. / Fisheries Research 99 (2009) 97–105 105
Acknowledgements
This research was part of the Programa Nacional de Avaliacãodo Potencial Sustentável dos Recursos vivos da Zona EconômicaExclusiva—REVIZEE, funded by the Ministério do Meio Ambiente(MMA) and the Secretaria da Comissão Interministerial para osRecursos do Mar (SECIRM). We thank André Vasconcelos, KeniaCunha, Elton Nunes, Kátia Freire, Denis Hellebrandt, MarceloNóbrega, Moustapha Diedhiou, Roberto Kobayashi, Sergio Rezende(DTI/CNPq), Simone Teixeira, and several students for their assis-tance in data collection, and Flávia Lucena for helpful comments onan earlier draft.
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