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Small-scale shrimp fisheries bycatch: A multi-criteria approach for data-scarse situations
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Marine Policy journal homepage: www.elsevier.com/locate/marpol
Small-scale shrimp fisheries bycatch: A multi-criteria approach for datascarse situations Adriana Rosa Carvalhoa,∗, Maria Grazia Penninoa,b,c, Jose Maria Bellidob,c, George Olavod a
Fisheries Ecology, Management and Economics Unit – FEME, Ecology Department, The Federal University of Rio Grande do Norte, Natal, Brazil Instituto Español de Oceanografía (The Spanish Oceanographic Institute), Centro Oceanográfico de Murcia (Murcia Oceanography Centre), San Pedro del Pinatar, Spain c Statistical Modeling Ecology Group (SMEG), Departament d'Estadística i Investigació Operativa (EIO Department), Universitat de València (The University of Valencia), C/Dr. Moliner 50, Burjassot. 46100 Valencia, Spain d Laboratory of Fishing Biology – LABPESCA, Earth Sciences and Environment Modeling Program (PPGM), State University of Feira de Santana, Bahia, Brazil b
ARTICLE INFO
ABSTRACT
Keywords: Bayesian models Fishers' behavior Economic incentives
Bycatch and discards from small-scale fisheries (SSF) are usually ignored when compared with industrial fisheries, not only by policy-makers, but also by scientists. Therefore, SSF social, economic and ecological impacts are poorly known and especially in the context of incidental catches, regardless of whether they become bycatch or discards. Such neglect is worrisome due to the role that SSF play in food security and poverty alleviation, particularly in coastal and rural communities in developing countries. In this study, a combination of sampling data and the fishers' behavior (specifically the basis of their decision on where to fish) were used. Bayesian models were applied to understand which variables affect the bycatch per unit effort (BPUE) variation in the shrimp SSF of the southern coast of the state of Bahia (Brazil). Results highlighted how BPUE variability is affected by a set of factors as well as by the specific fisher behavior. In the case of the shrimp fishery assessed here, economic incentives do not influence the decision to land the bycatch, since landed values decrease when bycatch increases. Shadow subsistence values (i.e. non-market values assigned to the consumption value) and the employ of fish as currency added an economic livelihood component to the complex decision regarding bycatch. Mitigation measures for SSF management strategies should be implemented at multiple stages of the bycatch and discarding process, both in the selection of the fishing grounds and the local economic role of the incidentally caught species.
1. Introduction
and non-living material [6]. On the coast of Brazil, most shrimp fishing is performed by smallscale fisheries (SSF), located along the tropical and sub-tropical shelves [7]. Bycatch and discards from SSF are usually ignored when compared with industrial fisheries, not only by policy-makers, but also by scientists partly due to the lack of scientific datasets (Silvano and Begossi, 2012). Furthermore, there is also a lack of perceived urgency regarding the bycatch and discarding problem [8]. Thus, the fishers' decision when choosing areas to fish may not be guided by the opportunities to protect or avoid the bycatch species. However, such decisions are not random. Instead they have long been motivated by internal variables (such as individual biases, values, morals, etc.), external variables (situational/
In tropical and subtropical regions worldwide the fishing of the Penaeidae family is an ancient and important activity performed both by artisanal and industrial fisheries [1,2]. The overall increase in shrimp catches has brought about the need to manage these fisheries adequately as there is evidence of overfishing in some of the world's shrimp stocks such as white shrimp, pink shrimps and brown shrimps [3,4]. In addition, shrimp trawling is considered one of the least selective fishing activities, in which a mixture of many other species contributes to the catch [5]. The trawling affects several species that fishers do not intend to catch, including fish, turtles, marine mammals, other animals
∗
Corresponding author. E-mail address: [email protected] (A.R. Carvalho).
https://doi.org/10.1016/j.marpol.2019.103613 Received 26 September 2018; Received in revised form 20 May 2019; Accepted 11 July 2019 0308-597X/ © 2019 Published by Elsevier Ltd.
Please cite this article as: Adriana Rosa Carvalho, et al., Marine Policy, https://doi.org/10.1016/j.marpol.2019.103613
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environmental circumstances, social/peer influences, etc.), by target species availability and its economic returns [9–11] and by the control beliefs everyone has or feels they have when behaving in a certain way, including their perceived access to resources, skills or opportunities [12,18]]. Even if key beliefs or social and cultural factors that combine to influence human behavior are not explicit, their use as an implicit factor may reveal linked mechanisms behind behavioral responses and advance our understanding of the role of human behavior in socioecological systems [13]. From the 1990s to the present day, despite the growing number of studies seeking to understand fishers' behavior mainly regarding ecological contexts, environmental conditions and management action [13–17], none have modeled the fisher’s decision as a random factor in order to deepen understanding on the bycatch and discards issue and on which factors influence the bycatch process in SSF. Under the theory of reasoned action [12], rewards reinforce any behavior, while penalties deceased attitudes. Following this tenet, if shrimp fishery returns good catches, attitudes towards fishing are automatically reinforced regardless any conscious awareness towards the bycatch. In short, beyond the usual information required to manage any fisheries, shrimp fisheries also need rules and strategies for understanding and averting bycatch, because the economic incentives for shrimp trade are likely to make fishers disregard the wicked problem of bycatch. Yet, if the market economically reward for the species caught unintentionally along with the targeted species, economic incentives will reinforce the bycatch. At whichever level of exploitation, policies to inform and manage bycatch should include monitoring of bycatch rates to, at least, inform which groups and how many of non-target species are caught, understand impacts on bycatch populations and incorporate strategies to minimize the bycatch [19,20]. Even though many development countries are unable to inform bycatch and comply with rules to reduce it due to lack of monitoring and enforcement capacity [21,22], they will be compulsorily guided to improve management capacity to be able to comply with import regulations such as U.S. Marine Mammal Protection Act (MMPA) and the European Union’s (EU) Regulation to prevent illegal, unreported and unregulated (IUU) fishing into the Europe [22,23]. Otherwise, the practice of unregulated fishing and/or unaware bycatch may pose economic hardship to poor countries dependent on seafood exports. Within this context, the objective of this study is to combine landing data and the fisher’s choice of fishing grounds (as a behavior factor) to assess which economic, environmental, spatial and fishing factors affect bycatch in shrimp SSF. In particular, the shrimp SSF of the southern state of Bahia in Brazil was used as a case study. This state is the third largest fish producer in Brazil and the largest producer in the Northeast region. Shrimp fishery in northeast Brazil employs roughly 100,000 people who depend directly or indirectly of the activity as livelihood. Fishing along Bahia coast is traditionally artisanal and is responsible for the direct and indirect employment of approximately 7000 people. Bycatch from shrimp fishery in the region affects predominantly nontarget juvenile fish species and it is scarcely registered and poorly understood. Our intention is to provide alternative, basic, reliable and cheap information for bycatch and discard management that can be applied worldwide, where there is limited information available, and most importantly where there is no other source of information.
the major fishing activity [24,25,34] (Fig. 1). Even though local fishers operate the trawling under the same method and similar incentives (for instance, by oil and salary payment by government during closed season), in the state of Bahia, likewise in the whole fishery in Brazil, there has been serious discontinuity in systematic landing monitoring and understanding on the marine resources availability, biology and ecology for years [10,26,27]. Such uncertainty curtails the claim and access to fishery policies by fishers. The main problems arising from this scenario are the lack of fishing surveys and management, which hamper the knowledge on fish stock and catch variability, on the number of fishers operating and on their needs, worsening the lack of information on the economic contribution of this fishery and its ecological status currently and in the future. In these communities, shrimp SSF target three main species: Atlantic seabob shrimp (Xiphopenaeus kroyeri); southern brown shrimp (Farfantepenaeus subtilis) and pink shrimp (Farfantepenaeus brasiliensis). In the fishing communities of the southern coast of the state of Bahia addressed here, there are around 1000 shrimp boats operating [11,28]. Among the fishing communities assessed, fishery is reasonably homogeneous. Two fishers/boat perform this fishery in motorized artisanal boats operating in shallow and coastal waters [29]. Trawling is performed at an average depth of 24m by nets ranging from 12 to 30m long (mesh size mainly ≈20). Fishing trips last from seven to 12 h to reach the fishing grounds aiming to search mainly for Atlantic seabob shrimp [30]. Many authors concede that policies toward to bycatch management require monitor bycatch rates, assess impacts on bycatch populations and apply strategies to minimize the bycatch [11,22,27]. However in Brazil, up to now there are few studies on shrimp fishery bycatch [10,11,31,33–39] and only three quantified and qualified bycatch composition in the northeastern [11,31,32]. The flawed monitoring of fisheries in the entire country poses serious limitation to the management of fishing resources and all fishing activities, including shrimp fisheries. Notwithstanding, as current management rule, the shrimp fisheries studied here and some others (not all) through the country are regulated by seasonal closed seasons according to the spawning peaks known for shrimp species. The closed season for shrimp fishing in the area assessed here occurs twice annually (01 April - 15 May and 15 September - 31 October [40]. The most recent management initiative under implementation is the FAO project launched in Brazil for bycatch reduction (Rebyc phase II) [41], through the incentive for using By-catch Reduction Devices (BRD) [42]. However, the project is being freshly developed and the BRD is under assays in Pernambuco, where there is some scientific information on bycatch [see 11, 32]. In Bahia state, the first meetings to implement the project are still to be planned in 2019. The climate is characterized as tropical humid, with three dry months during the year (August, September and October). May is the wettest month, with a mean precipitation value of 8.3 mm per day [43]. September is the driest month, with a mean precipitation value of 2.6 mm per day. The temperature varies from 23.7 °C in the winter to 26.7 °C during the summer. 2.2. Data collection Landing data applied here has been recorded daily through the participatory Monitoring of Fishing Landings Project performed by the Petrobras in the region of Camamu-Almada sedimentary basin, under the coordination of the Laboratory of Fisheries Biology (State University of Feira de Santana). Harbor observers from the project conducted interviews with fishers to record information on catch (in kg) of target and bycatch species, fishing effort and on several characteristics of each fishery operation and of the regional handling of fishing. Between January 2008 and December 2016, a total of 4048 fishing landing interviews were performed. Specifically, 3851 in Sao Francisco,
2. Material and methods 2.1. Study area This study was conducted in four artisanal fishing communities in the southern coast of the state of Bahia, in Brazil (i.e., Barra Grande in Marau municipality, Guaibim in Valença, Ilha do Contrato in Igrapiuna and São Francisco in Nilo Peçanha municipalities), located between the latitudes of 13°00′S and 14°30′S and where shrimp SSF constitute one of 2
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Fig. 1. Map of the study area and landing harbor locations.
2.3. Statistical analysis
592 in Barra Grande, 36 in Guaibim and 17 in Ilha do Contrato. Since an overview in the data from the four communities assessed here indicated that the target catch and bycatch statistics varied markedly between different vessels, we computed the catch per unit effort (CPUE) and bycatch per unit effort (BPUE) as the total catch and bycatch in a fishing operation (in kg), and standardized it per number of fishers and per haul duration (in days). In particular, three different CPUE were computed, one for each main target species (i.e., Atlantic seabob shrimp, southern brown shrimp and pink shrimp). Five categories were created to group fishery operation features: (1) fishing characteristics - type of boat, size of the net used, liters of gasoil consumed; (2) spatial - name of fishery ground, landing harbor; (3) environmental - bathymetry of the fishery operation; (4) economic – value of the catch, value of the gasoil consumed; (5) temporal – day, month and year of the fishery operation. A mixture of juvenile fish species or rays comprised the bycatch landed. However, rays were recorded in just 0.07% of the landings.
To better account for sampling uncertainties we followed the approach of [44] to model BPUE variation using a hierarchical Bayesian Generalized Linear Model. Log-transformed BPUE were used to downweight extreme values, to improve normality and to ensure a better fit of the models. A total of fourteen potential variables have been considered to account for BPUE variation, and these are listed in Table 1. Collinearity between explanatory variables was checked using a draftsman’s plot and the Pearson correlation index using the corrplot package [45] in R software [46]. In addition, a random factor that represents the fisher’s effect for each fishery operation was included as a possible predictor. Indeed, the remaining potential source of BPUE variation could be due to differences caused by a specific fisher behavior or unobserved gear characteristics. Ignoring such non-independence in the data may lead to invalid statistical inference. Then, in order to remove bias caused by fisher-specific differences in fishing operations, a random fisher effect was included. 3
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Table 1 Summary of variables included in Bayesian models as potential fixed effects influencing the BPUE variation. Variable
Description
Type of boat Net size Liters of gasoil Fishery ground Landing harbor Bathymetry Catch value Gasoil value Day Month Year CPUE of Atlantic seabob shrimp CPUE of southern brown shrimp CPUE of pink shrimp
Categorical. Two Levels: motorized and canoe Continuous: size of the net implemented during the fishery operation Continuous: liters of gasoil consumed during the fishery operation Categorical: common name of the fishery ground Categorical: Names of the harbor in which the catch was landed Continuous: Mean depth of the fishery operation. In meters Continuous: Value of the catch in Brazilian currency – R$) Continuous: Value of the liters of gasoil consumed during the fishery operation (in Brazilian currency - R$) Cyclic: Day of the fishery operation Factor: Month of the fishery operation (10 levels) as in April and October the shrimp fishing is closed Factor: Year of the fishing operation (2008–2016) Continuous: In Kg Continuous: In Kg/number of fisher per day Continuous: In Kg/number of fisher per day
For the parameters involved in the fixed effects, non-informative Gaussian distributions were used with a mean of zero and a variance of 100. All possible combinations of variables described in Table 1 were tested using both backwards and forwards approaches and the Watanabe-Akaike information criterion (WAIC) [47] to select relevant variables. The final model was selected on the base of the lowest WAIC and contained only relevant predictors (i.e., those predictors with 95% credibility intervals not including zero). Bayesian models were fitted using the integrated nested Laplace approximation (INLA) methodology and software [48] implemented in R.
The Bayesian model of the BPUE selected for its best fit (based on the lowest value of WAIC), included bathymetry, the fishery ground, CPUE of the pink shrimp and of the Atlantic seabob shrimp, the month of the fishery operation and the landed value (Table 2). In addition, the random fisher effect was also relevant for BPUE variability (Table 2). Specifically, the bathymetry of the fishery operation was one of the most important factors highlighting a negative relationship (posterior mean = - 0.51, IC95% [-0.02; −0.98]), i.e., BPUE is lower in shallow waters in the area (main depths ranges from 8 to 30m). The ‘Lama de Itacaré’ fishery ground was the one with the highest estimated BPUE with respect to the reference level (‘Costa do Pratigi’) (posterior mean = 0.43, IC95% [0.14; 0.82]) (Fig. 3). On the contrary, ‘Meio da Lama’ was the fishery ground with the lowest BPUE estimation with respect to the reference level (posterior mean = −0.37, IC95% [-0.04; −1.02]) (Fig. 3). Both CPUE measures presented a positive relationship, meaning that higher BPUE (kg/day) are recorded when the pink and Atlantic seabob shrimp catches are higher (posterior mean = 0.43, IC95% [0.09; 0.75]; and posterior mean = 0.23, IC95% [0.06; 0.62] respectively). Results showed that March and August are the months with the highest estimated BPUE (posterior mean = 0.64; 95% CI = [0.12, 1.08]; posterior mean = 0.18, IC95% [0.02; 0.59] respectively) with respect to the reference level (January) (Fig. 4). Conversely, February showed a lower BPUE value than the reference level (posterior mean = −0.29; 95% CI = [-0.02, −0.64]) (Fig. 4). Finally, the landed value of the catch presented a negative relationship with BPUE variability, indicating that the highest economic value is achieved by the catches when less BPUE is recorded (posterior mean = −0.19; 9 5% CI = [-0.03, −0.44]).
3. Results As showed in the cross-correlation matrix, variables were not highly correlated (r < 0.6, p-value = 0.05), and thus, all have been considered in further analyses (Fig. 2).
4. Discussion In this study, the understanding of on which factors affect BPUE variability in shrimp SSF was advanced using Bayesian models that pooled sampled data with the fisher effect accounted as a random factor. It is worth to say that the introduction of fisher effect in the model as a random effect does not imply that fisher behavior is random. It assumes, as a matter of fact, that certain variability could be present in the data due to individual fisher behaviors. The random effect unveil if this variability due to individual fisher behavior is relevant or not for BPUE. Our results highlight how BPUE variability in the southern coast of the Bahia state is influenced by a combination of fishing characteristics, environmental, economic and temporal factors, as well as by the specific fisher behavior. Amidst the range of factors, bathymetry was the one that influenced BPUE variation the most. Indeed, the bathymetric range of the fishery operations was strongly related to the bathymetric range of the shrimp species habitats. Atlantic seabob shrimp for instance is distributed in shallow waters, mainly in bays and
Fig. 2. Cross-correlation matrix of the explicative variables included in the model.
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Table 2 Model comparison of some of the most relevant models for BPUE variation in the southern Bahia state (Brazil).Watanabe-Akaike information criterion (WAIC) scores measure goodness-of-fit. Smaller scores represent better models. Parameters with asterisks (*) were statistically relevant. Acronyms are NS = net size; TP = Type of boat; LG = Liters of gasoil; FG = fishery ground; LH = Landing harbor; B = bathymetry of the fishery operation; (4) VC = value of the catch; VG = value of the gasoil; D = day; M = month; Y = year of the fishery operation; CPUEps = CPUE of pink shrimp; CPUEASS = CPUE of Atlantic seabob shrimp; CPUESBS = CPUE of Southern brown shrimp; RF = random fisher effect. Model 1* 1* 1* 1* 1* 1* 1* 1*
+ + + + + + + +
WAIC
B *+ NS + TS + LG + FG* + LH + VC* + VG + D + M* + Y + CPUEps *+ CPUEASS *+ CPUESBS + RF* NS + TS + LG + FG* + LH + RF* D + M* + Y VC* + VG B* RF* CPUEps *+ CPUEASS *+ CPUESBS B *+ FG* +VC* + M* + CPUEps *+ CPUEASS *+ RF*
estuaries along the coast [48]. Similarly, juvenile pink shrimp inhabit shallow nearshore areas (nursery habitats), though adults are distributed in offshore spawning grounds [49]. It is worthy of note that the fishery ground with the highest BPUE concentration (Lama de Itacaré) was also the one with the highest CPUE values of pink and Atlantic seabob shrimp species. Indeed, the results highlighted a direct and positive relationship between the CPUE of these species and the BPUE; meaning that more catch implies an increase in BPUE. Conversely, landed value is negatively correlated to BPUE indicating that the total value of landing is higher when few bycatch is caught and more shrimp is being landed. It is important to bear in mind that only tiny, and therefore low-valued species comprise the bycatch. Furthermore, as fishers go to fish where they know they can find a high concentration of their target species, bathymetry is the environmental variable that reflects the overlap between target and non-target species' preferential habitat. In the southern of Bahia state, non-target species are mainly low size and juvenile fish species caught as bycatch. Brazil, like other developing countries, does not monitor fishing bycatch landings and lack the scientific data to properly understand and manage fisheries [7]. Currently there are only three studies available on the qualitative and quantitative aspects of bycatch in the northeastern [11,31,32] and they do no refer to the shrimp fishery assessed, but to fishing grounds far from 800 to 1300 km. The scant research on bycatch in the entire northeastern coast,
423 399 418 402 405 416 387 366
Fig. 4. Boxplot of the BPUE (kg/day) variation with respect to the month in which the fishery operation was performed.
demand the use of methods to uncover the view on non-target species landed and to make bycatch risk assessment spatially explicit through a site-specific approach. There are concerns that, in the search for sustainability, spatial assessment leads to fishing policies toward create marine protected areas that may not encompass the core habitat of
Fig. 3. Boxplot of the BPUE (kg/day) variation with respect to the fishery grounds in the southern Bahia state (Brazil). 5
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species under bycatch risk, unnecessarily stating fixed time-area closures for fishing activity [50,51]. It should be considered that any economic losses posed to small-scale fishers by fishing restrictions, might compromise the opportunities for compliance. The counter-productive effect for fisheries management have to be taken into account, mainly considering the general poor or lax enforcement capacity in developing countries [52] and the wish for more participatory fishing policy decisions and less command and control regulations globally [53]. We consider the use of spatially explicit ecological and economic goals [54] and the near real-time spatial tools [51] as possible solution to overcome problems when deciding on suitable areas for fishing, to return reasonable economic earns and to reduce bycatch. Combined, these research tools allow the searching for sites that meet the economic importance of fisheries with their negative ecological impact and allow the use of dynamic ocean management approaches by daily predicting relative catch and bycatch probabilities. Although they are promising tools, the paucity of data makes their use challenging mainly for small-scale fisheries in developing countries. Specially to apply dynamic ocean management approaches, the bycatch composition should be precisely known. As so far we do not pursued data to allow the identification of the fish species caught as bycatch in the shrimp fishery we assessed, we would not be able to perform the spatial dynamic approach. It is important to bear in mind, however, that as far as we know, most of the bycatch from the shrimp fishery in the southern of Bahia state is landed. It is reasonable to assume that fishers want to discard the parts of the catch that cannot be sold without a loss, but more importantly they retain those species with higher ex-vessel value. How fishing decisions contribute to the economic value of landings is an important economic dimension of the fishery [55] and it is worthy of consideration when taking bycatch into account. Clearly, some economic incentives exist which influence the shrimp fishers' decision to keep and land the portion of tiny fish species from bycatch, which is unwanted and untradeable by the market. Such an incentive could be the high value reached by the bycatch species, the highly valued black market to trade species restricted by law [56], or otherwise, the guarantee that small lowvalued individuals will be bought up by the local market. In the case of the shrimp fishery assessed here, economic incentives do not influence the decision to land the bycatch, since there is not a black market steering the landed values, and these values decrease when bycatch increases. Economic incentives on land likely affecting fishing behavior on board [55], in this case was exclusively the shrimp price since local market do not regulate the landing of the bycatch. We were not able to somehow valuate the bycatch, as fish composition and the price per species (in the tradable size) were not available in the dataset. Following our assessment presented here, the values to be deemed and accounted are now being collected. Nonetheless, we were able to identify on harbor, how value might be aggregated to the bycatch through its flow in the supply chain.” Bycatch has value as barter for services related to the fishery and for consumption when taken home for family consumption and donated at the harbor or in the fisher’s neighborhood. The likely significant shadow subsistence value [57] and the use of fish as currency [58], add an economic livelihood component to the complex decision regarding bycatch. Economic factors are of importance in determining BPUE variability and could determine the profit to be obtained from fishing activities, and drive the compliance behavior of fishers [59]. When bycatch does not add significant value to landings, and any enforcement is present in the area, the variability of BPUE may be mainly driven by other factors, as evidenced in the shrimp fishery reported here. Finally, the bycatch assessment approach used here is twofold. First, the behavior of certain fishers or the adoption of such behavior to some extent mirror the local ecological knowledge with regards to their individual wisdom, the fishing environment and fishery resources in a conceptual context. In the context of the study area, fisher’s wisdom
and behavior are driven by the economic incentives for good shrimp catches. Bycatch is this case, is like a side effect used as barter for services or for consumption. Secondly, from a methodological point of view the use of Bayesian models together with fisher behavior includes assumptions on fisher behavior without inherently needing precise data gathering on fishers. It could be an extremely powerful approach given that they quantify different sources of uncertainty and provide estimation that could also become much more accurate and consistent with reality in data-scarse and data-poor situations. Discarding is a process decided on board based on the specific fisher behavior that could be influenced by the size of the catch, market prices of species and/or taking legal constraints into account [44]. In our results, the random fisher effect collected this hidden variability that otherwise could not be analyzed. This approach may also turns out into previous information to carry on further investigations on fisher’s knowledge, when such factor was proven an important driver of bycatches. Besides, the evidences that increasing bycatch leads to lower landed value, call the attention to the need for understanding the shadow subsistence values hidden by locals consumption or trade by the economic market and value chain operating locally. Acknowlwdgement We thank the Instituto Español de Oceanografia (IEO) Centro Oceanografico de Murcia for hosting this study. We are grateful to the editors and reviewers, and to Petrobrás for supporting the participatory Monitoring of Fishing Landings Project performed in the region of Camamu-Almada sedimentary basin. A.R.C. thanks the Brazilian National Research Council (CNPq) for the productivity grant. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpol.2019.103613. References [1] R. Gillett, Global Study of Shrimp Fisheries, FAO Fisheries Technical Paper 475 FAO, Rome, 2008, p. 331. Available on line at http://www.fao.org/docrep/011/ i0300e/i0300e00.htm. [2] J. Musiello-Fernandes, C.A. Zappes, M. Hostim-Silva, Small-scale fisheries of the Atlantic seabob shrimp (Kiphopenaeus kroyeri): continuity of commercialization and maintenance of the local culture through making public policies on the Brazilian coast, Ocean Coast. Manag. 155 (2018) 76–82 https://doi.org/10.1016/j. ocecoaman.2018.01.033. [3] N.O. Leite Jr., M. Petrere Jr., Stock assessment and fishery management of the pink shrimp Farfantepenaeus brasiliensis Latreille, 1970 and F. paulensis Pérez-Farfante, 1967 in southeastern Brazil (23 to 28 S), Braz. J. Biol. 66 (1B) (2006) 263–277 https://doi.org/10.1590/S1519-69842006000200009. [4] P.F.M. Lopes, M.G., Pennino, F. Freire, Climate change can reduce shrimp catches in equatorial Brazil, Reg. Environ. Chang. 18 (1) (2018) 223–234 https://doi.org/10. 1007/s10113-017-1203-8. [5] R.M. Cook, M.R. Heath, Population trends of bycatch species reflect improving status of target species, Fish Fish. 19 (2018) 455–470 https://doi.org/10.1111/faf. 12265. [6] R.L. Lewison, L.B. Crowder, B.P. Wallace, J.E. Moore, T. Cox, et al., Global patterns of marine mammal, seabird, and seaturtle bycatch reveal taxa-specific and cumulative megafauna hotspots, PNAS Proc. Natl. Acad. Sci. 111 (2014) 5271–5276 www.pnas.org/cgi/doi/10.1073/pnas.1318960111. [7] R.A.M. Silvano, A. Begossi, Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management, Neotrop. Ichthyol. 10 (7) (2012), https://doi.org/10.1590/S167962252012000100013. [8] S. Eayrs, S.X. Cadrin, C.W. Glass, Managing change in fisheries: a missing key to fishery-dependent data collection? ICES J. Mar. Sci. Journal du Conseil 72 (4) (2015) 1152–1158 https://doi.org/10.1093/icesjms/fsu184. [9] R.R.M. Martins, C. Monteiro-Neto, Economic trends of industrial double-rig bottom trawlers in Southeastern Brazil, Mar. Policy 90 (2018) 125–136. [10] J.A. Kolling, A.O. Ávila-da-Silva, J.A. Quintanilha, Spatiotemporal evaluation of Xiphopenaeus kroyeri (Heller, 1862) in a region of the Southwest Atlantic shelf, Cont. Shelf Res. 177 (2019) 50–58 http://doi.org/10.1016/j.csr. 2019.02.007. [11] C.A.B. Silva-Júnior, A.S. Lira, L.N. Eduardo, A.P. Viana, F. Lucena-Frédou, T. Fredóu, Ichthyofauna bycatch of the artisanal fishery of Penaeid shrimps in Pernmbuco, northeastern Brazil, Bo. Inst. Pesca 45 (1) (2019) e435 http://doi:10.
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Cintra, Carcinofauna acompanhante da pesca artesanal do camarão-sete-barbas ao largo da foz do rio São Francisco (Alagoas e Sergipe, Brasil), Acta Fish. Aquat. Resour. 4 (1) (2016) 1–10. [40] BRASIL. Instrução normativa MMA No. 14, de 14 outubro de 2004. DOU 15/10/ 2004. Estabelece os períodos de defeso, espécies distancia da costa e municipios para a pesca do camarão rosa (Farfantepenaeus subtilis e Farfantepenaeus brasiliensis), camarão setebarbas (Xiphopenaeus kroyeri) e camarão branco (Litopenaeus schmitti), (2004) Available (in Portuguese) at www.mpma.mp.br/arquivos/COCOM/ arquivos/centros_de_apoio/cao_meio_ambiente/calendario_pesca/IN14_2004 , Accessed date: 30 April 2018. [41] FAO – Food and Agriculture Organization of the United Nations, FAO Fisheries and Aquaculture Department, REBYC Phase II Reduction of Environmental Impact from Tropical Shrimp Trawling through the Introduction of by-Catch Reduction Technologies and Change of Management. 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Contents lists available at ScienceDirect
Marine Policy journal homepage: www.elsevier.com/locate/marpol
Small-scale shrimp fisheries bycatch: A multi-criteria approach for datascarse situations Adriana Rosa Carvalhoa,∗, Maria Grazia Penninoa,b,c, Jose Maria Bellidob,c, George Olavod a
Fisheries Ecology, Management and Economics Unit – FEME, Ecology Department, The Federal University of Rio Grande do Norte, Natal, Brazil Instituto Español de Oceanografía (The Spanish Oceanographic Institute), Centro Oceanográfico de Murcia (Murcia Oceanography Centre), San Pedro del Pinatar, Spain c Statistical Modeling Ecology Group (SMEG), Departament d'Estadística i Investigació Operativa (EIO Department), Universitat de València (The University of Valencia), C/Dr. Moliner 50, Burjassot. 46100 Valencia, Spain d Laboratory of Fishing Biology – LABPESCA, Earth Sciences and Environment Modeling Program (PPGM), State University of Feira de Santana, Bahia, Brazil b
ARTICLE INFO
ABSTRACT
Keywords: Bayesian models Fishers' behavior Economic incentives
Bycatch and discards from small-scale fisheries (SSF) are usually ignored when compared with industrial fisheries, not only by policy-makers, but also by scientists. Therefore, SSF social, economic and ecological impacts are poorly known and especially in the context of incidental catches, regardless of whether they become bycatch or discards. Such neglect is worrisome due to the role that SSF play in food security and poverty alleviation, particularly in coastal and rural communities in developing countries. In this study, a combination of sampling data and the fishers' behavior (specifically the basis of their decision on where to fish) were used. Bayesian models were applied to understand which variables affect the bycatch per unit effort (BPUE) variation in the shrimp SSF of the southern coast of the state of Bahia (Brazil). Results highlighted how BPUE variability is affected by a set of factors as well as by the specific fisher behavior. In the case of the shrimp fishery assessed here, economic incentives do not influence the decision to land the bycatch, since landed values decrease when bycatch increases. Shadow subsistence values (i.e. non-market values assigned to the consumption value) and the employ of fish as currency added an economic livelihood component to the complex decision regarding bycatch. Mitigation measures for SSF management strategies should be implemented at multiple stages of the bycatch and discarding process, both in the selection of the fishing grounds and the local economic role of the incidentally caught species.
1. Introduction
and non-living material [6]. On the coast of Brazil, most shrimp fishing is performed by smallscale fisheries (SSF), located along the tropical and sub-tropical shelves [7]. Bycatch and discards from SSF are usually ignored when compared with industrial fisheries, not only by policy-makers, but also by scientists partly due to the lack of scientific datasets (Silvano and Begossi, 2012). Furthermore, there is also a lack of perceived urgency regarding the bycatch and discarding problem [8]. Thus, the fishers' decision when choosing areas to fish may not be guided by the opportunities to protect or avoid the bycatch species. However, such decisions are not random. Instead they have long been motivated by internal variables (such as individual biases, values, morals, etc.), external variables (situational/
In tropical and subtropical regions worldwide the fishing of the Penaeidae family is an ancient and important activity performed both by artisanal and industrial fisheries [1,2]. The overall increase in shrimp catches has brought about the need to manage these fisheries adequately as there is evidence of overfishing in some of the world's shrimp stocks such as white shrimp, pink shrimps and brown shrimps [3,4]. In addition, shrimp trawling is considered one of the least selective fishing activities, in which a mixture of many other species contributes to the catch [5]. The trawling affects several species that fishers do not intend to catch, including fish, turtles, marine mammals, other animals
∗
Corresponding author. E-mail address: [email protected] (A.R. Carvalho).
https://doi.org/10.1016/j.marpol.2019.103613 Received 26 September 2018; Received in revised form 20 May 2019; Accepted 11 July 2019 0308-597X/ © 2019 Published by Elsevier Ltd.
Please cite this article as: Adriana Rosa Carvalho, et al., Marine Policy, https://doi.org/10.1016/j.marpol.2019.103613
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environmental circumstances, social/peer influences, etc.), by target species availability and its economic returns [9–11] and by the control beliefs everyone has or feels they have when behaving in a certain way, including their perceived access to resources, skills or opportunities [12,18]]. Even if key beliefs or social and cultural factors that combine to influence human behavior are not explicit, their use as an implicit factor may reveal linked mechanisms behind behavioral responses and advance our understanding of the role of human behavior in socioecological systems [13]. From the 1990s to the present day, despite the growing number of studies seeking to understand fishers' behavior mainly regarding ecological contexts, environmental conditions and management action [13–17], none have modeled the fisher’s decision as a random factor in order to deepen understanding on the bycatch and discards issue and on which factors influence the bycatch process in SSF. Under the theory of reasoned action [12], rewards reinforce any behavior, while penalties deceased attitudes. Following this tenet, if shrimp fishery returns good catches, attitudes towards fishing are automatically reinforced regardless any conscious awareness towards the bycatch. In short, beyond the usual information required to manage any fisheries, shrimp fisheries also need rules and strategies for understanding and averting bycatch, because the economic incentives for shrimp trade are likely to make fishers disregard the wicked problem of bycatch. Yet, if the market economically reward for the species caught unintentionally along with the targeted species, economic incentives will reinforce the bycatch. At whichever level of exploitation, policies to inform and manage bycatch should include monitoring of bycatch rates to, at least, inform which groups and how many of non-target species are caught, understand impacts on bycatch populations and incorporate strategies to minimize the bycatch [19,20]. Even though many development countries are unable to inform bycatch and comply with rules to reduce it due to lack of monitoring and enforcement capacity [21,22], they will be compulsorily guided to improve management capacity to be able to comply with import regulations such as U.S. Marine Mammal Protection Act (MMPA) and the European Union’s (EU) Regulation to prevent illegal, unreported and unregulated (IUU) fishing into the Europe [22,23]. Otherwise, the practice of unregulated fishing and/or unaware bycatch may pose economic hardship to poor countries dependent on seafood exports. Within this context, the objective of this study is to combine landing data and the fisher’s choice of fishing grounds (as a behavior factor) to assess which economic, environmental, spatial and fishing factors affect bycatch in shrimp SSF. In particular, the shrimp SSF of the southern state of Bahia in Brazil was used as a case study. This state is the third largest fish producer in Brazil and the largest producer in the Northeast region. Shrimp fishery in northeast Brazil employs roughly 100,000 people who depend directly or indirectly of the activity as livelihood. Fishing along Bahia coast is traditionally artisanal and is responsible for the direct and indirect employment of approximately 7000 people. Bycatch from shrimp fishery in the region affects predominantly nontarget juvenile fish species and it is scarcely registered and poorly understood. Our intention is to provide alternative, basic, reliable and cheap information for bycatch and discard management that can be applied worldwide, where there is limited information available, and most importantly where there is no other source of information.
the major fishing activity [24,25,34] (Fig. 1). Even though local fishers operate the trawling under the same method and similar incentives (for instance, by oil and salary payment by government during closed season), in the state of Bahia, likewise in the whole fishery in Brazil, there has been serious discontinuity in systematic landing monitoring and understanding on the marine resources availability, biology and ecology for years [10,26,27]. Such uncertainty curtails the claim and access to fishery policies by fishers. The main problems arising from this scenario are the lack of fishing surveys and management, which hamper the knowledge on fish stock and catch variability, on the number of fishers operating and on their needs, worsening the lack of information on the economic contribution of this fishery and its ecological status currently and in the future. In these communities, shrimp SSF target three main species: Atlantic seabob shrimp (Xiphopenaeus kroyeri); southern brown shrimp (Farfantepenaeus subtilis) and pink shrimp (Farfantepenaeus brasiliensis). In the fishing communities of the southern coast of the state of Bahia addressed here, there are around 1000 shrimp boats operating [11,28]. Among the fishing communities assessed, fishery is reasonably homogeneous. Two fishers/boat perform this fishery in motorized artisanal boats operating in shallow and coastal waters [29]. Trawling is performed at an average depth of 24m by nets ranging from 12 to 30m long (mesh size mainly ≈20). Fishing trips last from seven to 12 h to reach the fishing grounds aiming to search mainly for Atlantic seabob shrimp [30]. Many authors concede that policies toward to bycatch management require monitor bycatch rates, assess impacts on bycatch populations and apply strategies to minimize the bycatch [11,22,27]. However in Brazil, up to now there are few studies on shrimp fishery bycatch [10,11,31,33–39] and only three quantified and qualified bycatch composition in the northeastern [11,31,32]. The flawed monitoring of fisheries in the entire country poses serious limitation to the management of fishing resources and all fishing activities, including shrimp fisheries. Notwithstanding, as current management rule, the shrimp fisheries studied here and some others (not all) through the country are regulated by seasonal closed seasons according to the spawning peaks known for shrimp species. The closed season for shrimp fishing in the area assessed here occurs twice annually (01 April - 15 May and 15 September - 31 October [40]. The most recent management initiative under implementation is the FAO project launched in Brazil for bycatch reduction (Rebyc phase II) [41], through the incentive for using By-catch Reduction Devices (BRD) [42]. However, the project is being freshly developed and the BRD is under assays in Pernambuco, where there is some scientific information on bycatch [see 11, 32]. In Bahia state, the first meetings to implement the project are still to be planned in 2019. The climate is characterized as tropical humid, with three dry months during the year (August, September and October). May is the wettest month, with a mean precipitation value of 8.3 mm per day [43]. September is the driest month, with a mean precipitation value of 2.6 mm per day. The temperature varies from 23.7 °C in the winter to 26.7 °C during the summer. 2.2. Data collection Landing data applied here has been recorded daily through the participatory Monitoring of Fishing Landings Project performed by the Petrobras in the region of Camamu-Almada sedimentary basin, under the coordination of the Laboratory of Fisheries Biology (State University of Feira de Santana). Harbor observers from the project conducted interviews with fishers to record information on catch (in kg) of target and bycatch species, fishing effort and on several characteristics of each fishery operation and of the regional handling of fishing. Between January 2008 and December 2016, a total of 4048 fishing landing interviews were performed. Specifically, 3851 in Sao Francisco,
2. Material and methods 2.1. Study area This study was conducted in four artisanal fishing communities in the southern coast of the state of Bahia, in Brazil (i.e., Barra Grande in Marau municipality, Guaibim in Valença, Ilha do Contrato in Igrapiuna and São Francisco in Nilo Peçanha municipalities), located between the latitudes of 13°00′S and 14°30′S and where shrimp SSF constitute one of 2
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Fig. 1. Map of the study area and landing harbor locations.
2.3. Statistical analysis
592 in Barra Grande, 36 in Guaibim and 17 in Ilha do Contrato. Since an overview in the data from the four communities assessed here indicated that the target catch and bycatch statistics varied markedly between different vessels, we computed the catch per unit effort (CPUE) and bycatch per unit effort (BPUE) as the total catch and bycatch in a fishing operation (in kg), and standardized it per number of fishers and per haul duration (in days). In particular, three different CPUE were computed, one for each main target species (i.e., Atlantic seabob shrimp, southern brown shrimp and pink shrimp). Five categories were created to group fishery operation features: (1) fishing characteristics - type of boat, size of the net used, liters of gasoil consumed; (2) spatial - name of fishery ground, landing harbor; (3) environmental - bathymetry of the fishery operation; (4) economic – value of the catch, value of the gasoil consumed; (5) temporal – day, month and year of the fishery operation. A mixture of juvenile fish species or rays comprised the bycatch landed. However, rays were recorded in just 0.07% of the landings.
To better account for sampling uncertainties we followed the approach of [44] to model BPUE variation using a hierarchical Bayesian Generalized Linear Model. Log-transformed BPUE were used to downweight extreme values, to improve normality and to ensure a better fit of the models. A total of fourteen potential variables have been considered to account for BPUE variation, and these are listed in Table 1. Collinearity between explanatory variables was checked using a draftsman’s plot and the Pearson correlation index using the corrplot package [45] in R software [46]. In addition, a random factor that represents the fisher’s effect for each fishery operation was included as a possible predictor. Indeed, the remaining potential source of BPUE variation could be due to differences caused by a specific fisher behavior or unobserved gear characteristics. Ignoring such non-independence in the data may lead to invalid statistical inference. Then, in order to remove bias caused by fisher-specific differences in fishing operations, a random fisher effect was included. 3
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Table 1 Summary of variables included in Bayesian models as potential fixed effects influencing the BPUE variation. Variable
Description
Type of boat Net size Liters of gasoil Fishery ground Landing harbor Bathymetry Catch value Gasoil value Day Month Year CPUE of Atlantic seabob shrimp CPUE of southern brown shrimp CPUE of pink shrimp
Categorical. Two Levels: motorized and canoe Continuous: size of the net implemented during the fishery operation Continuous: liters of gasoil consumed during the fishery operation Categorical: common name of the fishery ground Categorical: Names of the harbor in which the catch was landed Continuous: Mean depth of the fishery operation. In meters Continuous: Value of the catch in Brazilian currency – R$) Continuous: Value of the liters of gasoil consumed during the fishery operation (in Brazilian currency - R$) Cyclic: Day of the fishery operation Factor: Month of the fishery operation (10 levels) as in April and October the shrimp fishing is closed Factor: Year of the fishing operation (2008–2016) Continuous: In Kg Continuous: In Kg/number of fisher per day Continuous: In Kg/number of fisher per day
For the parameters involved in the fixed effects, non-informative Gaussian distributions were used with a mean of zero and a variance of 100. All possible combinations of variables described in Table 1 were tested using both backwards and forwards approaches and the Watanabe-Akaike information criterion (WAIC) [47] to select relevant variables. The final model was selected on the base of the lowest WAIC and contained only relevant predictors (i.e., those predictors with 95% credibility intervals not including zero). Bayesian models were fitted using the integrated nested Laplace approximation (INLA) methodology and software [48] implemented in R.
The Bayesian model of the BPUE selected for its best fit (based on the lowest value of WAIC), included bathymetry, the fishery ground, CPUE of the pink shrimp and of the Atlantic seabob shrimp, the month of the fishery operation and the landed value (Table 2). In addition, the random fisher effect was also relevant for BPUE variability (Table 2). Specifically, the bathymetry of the fishery operation was one of the most important factors highlighting a negative relationship (posterior mean = - 0.51, IC95% [-0.02; −0.98]), i.e., BPUE is lower in shallow waters in the area (main depths ranges from 8 to 30m). The ‘Lama de Itacaré’ fishery ground was the one with the highest estimated BPUE with respect to the reference level (‘Costa do Pratigi’) (posterior mean = 0.43, IC95% [0.14; 0.82]) (Fig. 3). On the contrary, ‘Meio da Lama’ was the fishery ground with the lowest BPUE estimation with respect to the reference level (posterior mean = −0.37, IC95% [-0.04; −1.02]) (Fig. 3). Both CPUE measures presented a positive relationship, meaning that higher BPUE (kg/day) are recorded when the pink and Atlantic seabob shrimp catches are higher (posterior mean = 0.43, IC95% [0.09; 0.75]; and posterior mean = 0.23, IC95% [0.06; 0.62] respectively). Results showed that March and August are the months with the highest estimated BPUE (posterior mean = 0.64; 95% CI = [0.12, 1.08]; posterior mean = 0.18, IC95% [0.02; 0.59] respectively) with respect to the reference level (January) (Fig. 4). Conversely, February showed a lower BPUE value than the reference level (posterior mean = −0.29; 95% CI = [-0.02, −0.64]) (Fig. 4). Finally, the landed value of the catch presented a negative relationship with BPUE variability, indicating that the highest economic value is achieved by the catches when less BPUE is recorded (posterior mean = −0.19; 9 5% CI = [-0.03, −0.44]).
3. Results As showed in the cross-correlation matrix, variables were not highly correlated (r < 0.6, p-value = 0.05), and thus, all have been considered in further analyses (Fig. 2).
4. Discussion In this study, the understanding of on which factors affect BPUE variability in shrimp SSF was advanced using Bayesian models that pooled sampled data with the fisher effect accounted as a random factor. It is worth to say that the introduction of fisher effect in the model as a random effect does not imply that fisher behavior is random. It assumes, as a matter of fact, that certain variability could be present in the data due to individual fisher behaviors. The random effect unveil if this variability due to individual fisher behavior is relevant or not for BPUE. Our results highlight how BPUE variability in the southern coast of the Bahia state is influenced by a combination of fishing characteristics, environmental, economic and temporal factors, as well as by the specific fisher behavior. Amidst the range of factors, bathymetry was the one that influenced BPUE variation the most. Indeed, the bathymetric range of the fishery operations was strongly related to the bathymetric range of the shrimp species habitats. Atlantic seabob shrimp for instance is distributed in shallow waters, mainly in bays and
Fig. 2. Cross-correlation matrix of the explicative variables included in the model.
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Table 2 Model comparison of some of the most relevant models for BPUE variation in the southern Bahia state (Brazil).Watanabe-Akaike information criterion (WAIC) scores measure goodness-of-fit. Smaller scores represent better models. Parameters with asterisks (*) were statistically relevant. Acronyms are NS = net size; TP = Type of boat; LG = Liters of gasoil; FG = fishery ground; LH = Landing harbor; B = bathymetry of the fishery operation; (4) VC = value of the catch; VG = value of the gasoil; D = day; M = month; Y = year of the fishery operation; CPUEps = CPUE of pink shrimp; CPUEASS = CPUE of Atlantic seabob shrimp; CPUESBS = CPUE of Southern brown shrimp; RF = random fisher effect. Model 1* 1* 1* 1* 1* 1* 1* 1*
+ + + + + + + +
WAIC
B *+ NS + TS + LG + FG* + LH + VC* + VG + D + M* + Y + CPUEps *+ CPUEASS *+ CPUESBS + RF* NS + TS + LG + FG* + LH + RF* D + M* + Y VC* + VG B* RF* CPUEps *+ CPUEASS *+ CPUESBS B *+ FG* +VC* + M* + CPUEps *+ CPUEASS *+ RF*
estuaries along the coast [48]. Similarly, juvenile pink shrimp inhabit shallow nearshore areas (nursery habitats), though adults are distributed in offshore spawning grounds [49]. It is worthy of note that the fishery ground with the highest BPUE concentration (Lama de Itacaré) was also the one with the highest CPUE values of pink and Atlantic seabob shrimp species. Indeed, the results highlighted a direct and positive relationship between the CPUE of these species and the BPUE; meaning that more catch implies an increase in BPUE. Conversely, landed value is negatively correlated to BPUE indicating that the total value of landing is higher when few bycatch is caught and more shrimp is being landed. It is important to bear in mind that only tiny, and therefore low-valued species comprise the bycatch. Furthermore, as fishers go to fish where they know they can find a high concentration of their target species, bathymetry is the environmental variable that reflects the overlap between target and non-target species' preferential habitat. In the southern of Bahia state, non-target species are mainly low size and juvenile fish species caught as bycatch. Brazil, like other developing countries, does not monitor fishing bycatch landings and lack the scientific data to properly understand and manage fisheries [7]. Currently there are only three studies available on the qualitative and quantitative aspects of bycatch in the northeastern [11,31,32] and they do no refer to the shrimp fishery assessed, but to fishing grounds far from 800 to 1300 km. The scant research on bycatch in the entire northeastern coast,
423 399 418 402 405 416 387 366
Fig. 4. Boxplot of the BPUE (kg/day) variation with respect to the month in which the fishery operation was performed.
demand the use of methods to uncover the view on non-target species landed and to make bycatch risk assessment spatially explicit through a site-specific approach. There are concerns that, in the search for sustainability, spatial assessment leads to fishing policies toward create marine protected areas that may not encompass the core habitat of
Fig. 3. Boxplot of the BPUE (kg/day) variation with respect to the fishery grounds in the southern Bahia state (Brazil). 5
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species under bycatch risk, unnecessarily stating fixed time-area closures for fishing activity [50,51]. It should be considered that any economic losses posed to small-scale fishers by fishing restrictions, might compromise the opportunities for compliance. The counter-productive effect for fisheries management have to be taken into account, mainly considering the general poor or lax enforcement capacity in developing countries [52] and the wish for more participatory fishing policy decisions and less command and control regulations globally [53]. We consider the use of spatially explicit ecological and economic goals [54] and the near real-time spatial tools [51] as possible solution to overcome problems when deciding on suitable areas for fishing, to return reasonable economic earns and to reduce bycatch. Combined, these research tools allow the searching for sites that meet the economic importance of fisheries with their negative ecological impact and allow the use of dynamic ocean management approaches by daily predicting relative catch and bycatch probabilities. Although they are promising tools, the paucity of data makes their use challenging mainly for small-scale fisheries in developing countries. Specially to apply dynamic ocean management approaches, the bycatch composition should be precisely known. As so far we do not pursued data to allow the identification of the fish species caught as bycatch in the shrimp fishery we assessed, we would not be able to perform the spatial dynamic approach. It is important to bear in mind, however, that as far as we know, most of the bycatch from the shrimp fishery in the southern of Bahia state is landed. It is reasonable to assume that fishers want to discard the parts of the catch that cannot be sold without a loss, but more importantly they retain those species with higher ex-vessel value. How fishing decisions contribute to the economic value of landings is an important economic dimension of the fishery [55] and it is worthy of consideration when taking bycatch into account. Clearly, some economic incentives exist which influence the shrimp fishers' decision to keep and land the portion of tiny fish species from bycatch, which is unwanted and untradeable by the market. Such an incentive could be the high value reached by the bycatch species, the highly valued black market to trade species restricted by law [56], or otherwise, the guarantee that small lowvalued individuals will be bought up by the local market. In the case of the shrimp fishery assessed here, economic incentives do not influence the decision to land the bycatch, since there is not a black market steering the landed values, and these values decrease when bycatch increases. Economic incentives on land likely affecting fishing behavior on board [55], in this case was exclusively the shrimp price since local market do not regulate the landing of the bycatch. We were not able to somehow valuate the bycatch, as fish composition and the price per species (in the tradable size) were not available in the dataset. Following our assessment presented here, the values to be deemed and accounted are now being collected. Nonetheless, we were able to identify on harbor, how value might be aggregated to the bycatch through its flow in the supply chain.” Bycatch has value as barter for services related to the fishery and for consumption when taken home for family consumption and donated at the harbor or in the fisher’s neighborhood. The likely significant shadow subsistence value [57] and the use of fish as currency [58], add an economic livelihood component to the complex decision regarding bycatch. Economic factors are of importance in determining BPUE variability and could determine the profit to be obtained from fishing activities, and drive the compliance behavior of fishers [59]. When bycatch does not add significant value to landings, and any enforcement is present in the area, the variability of BPUE may be mainly driven by other factors, as evidenced in the shrimp fishery reported here. Finally, the bycatch assessment approach used here is twofold. First, the behavior of certain fishers or the adoption of such behavior to some extent mirror the local ecological knowledge with regards to their individual wisdom, the fishing environment and fishery resources in a conceptual context. In the context of the study area, fisher’s wisdom
and behavior are driven by the economic incentives for good shrimp catches. Bycatch is this case, is like a side effect used as barter for services or for consumption. Secondly, from a methodological point of view the use of Bayesian models together with fisher behavior includes assumptions on fisher behavior without inherently needing precise data gathering on fishers. It could be an extremely powerful approach given that they quantify different sources of uncertainty and provide estimation that could also become much more accurate and consistent with reality in data-scarse and data-poor situations. Discarding is a process decided on board based on the specific fisher behavior that could be influenced by the size of the catch, market prices of species and/or taking legal constraints into account [44]. In our results, the random fisher effect collected this hidden variability that otherwise could not be analyzed. This approach may also turns out into previous information to carry on further investigations on fisher’s knowledge, when such factor was proven an important driver of bycatches. Besides, the evidences that increasing bycatch leads to lower landed value, call the attention to the need for understanding the shadow subsistence values hidden by locals consumption or trade by the economic market and value chain operating locally. Acknowlwdgement We thank the Instituto Español de Oceanografia (IEO) Centro Oceanografico de Murcia for hosting this study. We are grateful to the editors and reviewers, and to Petrobrás for supporting the participatory Monitoring of Fishing Landings Project performed in the region of Camamu-Almada sedimentary basin. A.R.C. thanks the Brazilian National Research Council (CNPq) for the productivity grant. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpol.2019.103613. References [1] R. Gillett, Global Study of Shrimp Fisheries, FAO Fisheries Technical Paper 475 FAO, Rome, 2008, p. 331. Available on line at http://www.fao.org/docrep/011/ i0300e/i0300e00.htm. [2] J. Musiello-Fernandes, C.A. Zappes, M. Hostim-Silva, Small-scale fisheries of the Atlantic seabob shrimp (Kiphopenaeus kroyeri): continuity of commercialization and maintenance of the local culture through making public policies on the Brazilian coast, Ocean Coast. Manag. 155 (2018) 76–82 https://doi.org/10.1016/j. ocecoaman.2018.01.033. [3] N.O. Leite Jr., M. Petrere Jr., Stock assessment and fishery management of the pink shrimp Farfantepenaeus brasiliensis Latreille, 1970 and F. paulensis Pérez-Farfante, 1967 in southeastern Brazil (23 to 28 S), Braz. J. Biol. 66 (1B) (2006) 263–277 https://doi.org/10.1590/S1519-69842006000200009. [4] P.F.M. Lopes, M.G., Pennino, F. Freire, Climate change can reduce shrimp catches in equatorial Brazil, Reg. Environ. Chang. 18 (1) (2018) 223–234 https://doi.org/10. 1007/s10113-017-1203-8. [5] R.M. Cook, M.R. Heath, Population trends of bycatch species reflect improving status of target species, Fish Fish. 19 (2018) 455–470 https://doi.org/10.1111/faf. 12265. [6] R.L. Lewison, L.B. Crowder, B.P. Wallace, J.E. Moore, T. Cox, et al., Global patterns of marine mammal, seabird, and seaturtle bycatch reveal taxa-specific and cumulative megafauna hotspots, PNAS Proc. Natl. Acad. Sci. 111 (2014) 5271–5276 www.pnas.org/cgi/doi/10.1073/pnas.1318960111. [7] R.A.M. Silvano, A. Begossi, Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management, Neotrop. Ichthyol. 10 (7) (2012), https://doi.org/10.1590/S167962252012000100013. [8] S. Eayrs, S.X. Cadrin, C.W. Glass, Managing change in fisheries: a missing key to fishery-dependent data collection? ICES J. Mar. Sci. Journal du Conseil 72 (4) (2015) 1152–1158 https://doi.org/10.1093/icesjms/fsu184. [9] R.R.M. Martins, C. Monteiro-Neto, Economic trends of industrial double-rig bottom trawlers in Southeastern Brazil, Mar. Policy 90 (2018) 125–136. [10] J.A. Kolling, A.O. Ávila-da-Silva, J.A. Quintanilha, Spatiotemporal evaluation of Xiphopenaeus kroyeri (Heller, 1862) in a region of the Southwest Atlantic shelf, Cont. Shelf Res. 177 (2019) 50–58 http://doi.org/10.1016/j.csr. 2019.02.007. [11] C.A.B. Silva-Júnior, A.S. Lira, L.N. Eduardo, A.P. Viana, F. Lucena-Frédou, T. Fredóu, Ichthyofauna bycatch of the artisanal fishery of Penaeid shrimps in Pernmbuco, northeastern Brazil, Bo. Inst. Pesca 45 (1) (2019) e435 http://doi:10.
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