Latitudinal and bathymetric
abundance tendencies of
yellowtail snapper,
Lutjanus
chrysurus
(Bloch, 1791) (Teleostei,
Lutjanidae), caught off
northeastern Brazil
Marcelo N
ÓBREGAPostgraduate Program in Biological Oceanography,
Institute of Oceanography and Laboratory of Environmental Statistics, Federal University of Rio Grande do Sul (FURG), Cep: 96201-900, P.O. Box 474, Rio Grande, Brazil
Phone: 55(Brazil)-84-3342-4963 E-mail:[email protected]
Eduardo F
ERRANDISDepartment of Marine Sciences and Applied Biology, University of Alicante, Spain
Paul K
INASPostgraduate Program in Biological Oceanography
Laboratory of Environmental Statistics, FURG, Rio Grande, Brazil
Rosangela L
ESSADepartment of Fisheries and Aquaculture,
Federal Rural University of Pernambuco, Recife, Brazil
Abstract
Precise maps of habitats associated with the spatial distribution of fish populations are important tools for the conservation and management of fishery resources. In order to standardize mean of abundance indexes for the yellowtail snapper (Lutjanus chrysurus) caught off northeastern Brazil and identify spatial tendencies, generalized linear models (GLMs) and spatial statistical methods were used, based on catch and effort data from 1556 fishing trips. The data were collected between 1998 and 2000 from
landings of the artisanal fishing fleet operating bottom lines off northeastern Brazil. Two GLMs were used to model the probability of catching L. chrysurus and the expected catch weight for positive deployments. A standard relative-abundance index (CPUE) was established for the most frequent vessels in the catches, using factors and coefficients generated in the GLMs. Non-linear models were used to estimate CPUE tendencies in relation to latitude and depth. A parabolic cubic latitudinal model was adjusted to CPUE and latitude, describing greater abundance in the southern portion of the study area, with a tendency towards smaller abundance values towards lower latitudes. The non-linear bathymetric model estimated for CPUE indicated greater abundance between 25 and 50 m, with a tendency toward smaller abundance values at greater depths. A geographic model established through non-linear regression (combination of latitudinal and bathymetric models), with the aid of the ArcView GIS 3.1 program, enabled projection of the abundance tendencies of L. chrysurus on the continental shelf off northeastern Brazil. The resource studied shows a preference for depths between 25 and 50 m, probably because these depths offer favorable environmental factors for maintenance of the population. Thus, such areas should be prioritized for the conservation and management of this fishery resource in the region.
Key words
yellowtail snapper; Lutjanus chrysurus, spatial statistics.
1. Introduction
Catch and effort data from fisheries are used to establish relative-abundance indexes (CPUE). The use of these estimates as an index of abundance depends on the use of a method to remove the impact of factors influencing catches (fleet composition, time and area) that are not related to actual variations of abundance (Maunder and Punt, 2004). Historically, several methods have been developed to standardize catches (Gulland, 1956; Beverton and Holt, 1957; Robson, 1966; Honma, 1973). Generalized linear models (McCullaugh and Nelder, 1989) are currently used to standardize catch and effort data (Helser et al., 2004; Kimura 1981; Punt et al., 2000; Punt et al., 2001; Campbell 2004; Nishida and Chen, 2004; Maunder and Punt 2004; Xiao 2004).
As well as seeking to use methods that minimize noise in estimates of fish-stock abundance that are based on fisheries-dependent data, great emphasis has been given recently to the importance of spatial pattern, scale and variation as a component in the ecological process
(Petitgas 1993, Horne and Schneider 1995). The importance of heterogeneity in biological and physical resources is now recognized as an important factor in maintaining populations (Legendre and Fortin, 1989).
Recognizing and making predictions regarding the relationship between the dynamics of fish stocks and habitat occupation is fundamental to the effective evaluation and management of marine fish populations (Rubec et al., 2001). Precise maps of habitats associated with the spatial distribution of fish populations are becoming important tools for the management and protection of these habitats and the promotion of sustainable fishery activities (Rubec et al., 1998, Ault et al., 1999).
Spatially-referenced stock assessment has only recently been developed with the help of geographic information systems (GIS), characterized by organizing, analyzing and graphing data with complex and diverse geographic attributes (Nishida and Boot, 2001). There is a growing interest in the development of GIS in the marine area, particularly to view spatial-data sets and provide a platform for inventory valuation.
Caddy and Garcia (1986) described the first record of publishing GIS data on fisheries and aquaculture, and the use of this tool has grown rapidly during the past 90 years (Fisher, 2007) especially in applications that focused on the modeling and mapping of essential fish habitats as support for conservation and management (Fisher, 2010). The aim of the present study was to standardize mean abundance indexes for Lutjanus
chrysurus caught off northeastern Brazil and identify spatial-distribution
trends for these species in the region.
2. Materials and methods
Generalized linear models (GLMs) (McCullagh and Nelder, 1989) and spatial statistical methods were used based on catch (weight) and effort (hooks x time deployed in water) data from 1556 longline fishing trips. The data were collected between February 1998 and April 2000, during landings by the artisanal fleet (Plate 1) operating with bottom lines off northeastern Brazil.
Two GLMs were used to model the catch probability (binomial model and logistic link function) of L. chrysurus (1) and expected catch weight (2) for positive deployments (gamma model and identity link function) (McCullagh and Nelder, 1989; Venables and Dichmont, 2004). The level of significance was set at 5% for the identification of statistically
significant factors, coefficients and interactions that explain the variability in the response variables in the two classes of GLMs. The proposed models have the following covariables structure:
(1) Y = β + vessel + FP + depth + latitude + DC + month + (vessel × effort)
where β is an intercept term; FP is the fishing period and DC is the distance from the coast.
(2) Y = β + vessel + FP + depth + latitude + year + (vessel × effort)
where β is an intercept term and FP is the fishing period.
Following the estimation of the parameters in the two classes of GLMs, these models were used sequentially to estimate a standardized CPUE. For such, a type of vessel (motor boat) and the unit of effort (kg hook-1 day-1) were fixed; then the probability of the presence of the species in the first model and the expected catch for a unit of effort of motor boats conditioned to the presence of the species were estimated. The standardized unit of effort was the product of the two previous estimates. Thus, the standardized CPUE for motor boats was established in order to minimize variations in the abundance that may be caused by differences in the fishing power of different vessels. Motor boats were chosen because they represent 60% of landings sampled.
Linear (3), quadratic (4), cubic (5) (Seber and Wild, 2003) and nonlinear (6) (Ferrandis et al., 2010) regression models were tested to describe average trends in the standardized CPUE in relation to the depth and latitude of the fishing grounds. The level of significance for the relationship between variables was α = 0.05 [Printer: α is lower case Greek ‘alpha’] and the selection of models was determined by comparing coefficient-of-determination values (R2), with the best model chosen based on the higher value.
(3)
E
(
Y
)
=
β
0+
β
1x
(4) 2 2 1 0)
(
Y
x
x
E
=
β
+
β
+
β
(5) 3 3 2 2 1 0)
(
Y
x
x
x
E
=
β
+
β
+
β
+
β
in which
E
(Y
)
is the expected value of the response variable, β0 is the linear coefficient, β1 is the slope, β2 is the quadratic coefficient, β3is the cubic coefficient and
x
is the depth or latitude. (6) E(Y) = k (x
˗ α)(β ˗x
)/( 2x
˗ αx
+β)Where
E
(Y
)
is the expected value of the CPUE,x
the depth, {k, α, β} the parameters of the bathymetric trends, and where the interval (α, β) is the bathymetric rank of the species considered.A geographic model (Ferrandis et al., 2010) was established through nonlinear regression (7). This model articulates the expected abundance (CPUE) taking the two predictors in a multiplicative way. The use of the ArcView GIS 3.1 program and Spatial Analysis 1.0 module enabled projecting the trends of the bathymetric, latitudinal and geographic models for the abundance of L. chrysurus off northeastern Brazil.
(7) E(Y) = k L B
where Y is the CPUE index and L and B are the proposed latitudinal and bathymetric models.
3. Results
The species was recorded on 754 fishing trips (48.5%), with 9 t caught, representing 9.4% of the amount landed by the fleet (96 t). Weight per fishing trip ranged from 0.1 to 164.3 kg (mean = 11.95 kg; SD = 18.75 kg) (Figure 1). The fleet operated in depths that ranged from 7 to 340 m (mean = 64.7 m; SD = 29.3 m) and between latitudes 13° S and 2° S (Map 1).
Tables 1 and 2 display the summarized results of the GLMs and explanatory variables used in these models. The regressions tested for CPUE and latitude (Figure 2a) demonstrated that the quadratic parabolic model (Table 3) best described the mean abundance trends, indicating greater abundance values in the southern portion of the study area, with a diminishing tendency toward lower latitudes (Figure 2b).
Figure 1. Frequency distribution of the total weight of yellowtail snapper, Lutjanus chrysurus, landed by the bottom line fishery in the period of study.
Map 1. Sampling locations (red dots) and fishing areas of the bottom-line fleet in northeastern Brazil.
Among the models tested to describe mean CPUE trends for L.
chrysurus in relation to depth (Figure 4a), the nonlinear model (R2 =
0.231; P < 0.001) demonstrated the best fit (Table 4). The nonlinear bathymetric model (Figure 4b) estimated for CPUE indicated higher abundance values between 25 and 50 m, with a diminishing tendency at greater depths (Figure 4b).
# # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ### # # # # ## # # # # # # # # # # # ## # # # # # # # # # ## # # # ## # # # # ## # # # # # # # # #### # # # # # # # # # # # # # # # # # # # # # # # # ### # # # # # # # # # # # ## # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## ## ## # # # # # # # # # # # # # # # # # # # # ## # # # # # # # ## # # # # # # # # # # # # # # # # ### # # # # ## # # # # # # # ## # ## ## # # ## # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ### # ## # # # # # # # # # # # # ## # ## # # # # ## # # # # ## # # # # # # # # ## # # ## # # # # # # # # # # # # # # # # # # # # # # # ## # ## # # # # # # # # # ### # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # ## # ## # # # # # # # # # ## # # # # # # # ### # # # # # # # # # ## # # # # # # # # ## # # # # # # # # # # ## # # # # # # # # # # # # # # # ## # # # ### # ###### # ## # # # # # # # # # # # # ### # ### # # # # # # # ## # # ## # # # ## # # # ### # # # # # # # # # # # # # # # # # # # # ## # # ## # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ### # ## # ## # ## ## # # # # # # # ## # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # #### # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## # # # # # ## # # # # # # # # # # # # ## # ## # ## # # # # ### # # # # # # #### # ## ## # # # ### # # # # ##### ## # # # # ### ### # # # # # # ### # # # # # # #### # ### # # # # # # # # # # # # ## # # # ## # # # # # # # # ## # # #### # # # # # # ## # # ### # ## ### # # # # # # # # # # # # #### # # # ## # # # # # ##### # # # # # # # # # ## # # # # ### # ## # # # # # # ##### ## ## ## # ## # # ## ### # # # # # # # # #### # ## # ##### # # # ### # ### ## # # # ### ############# # ## # # ## # ### ## #### ### # # # # # # # # # # # ## # # # # # # # # # # # # # # # # # # #### # ## # # # ######### # # # # ### ## ## # # # # # # # # # # # ## # # ### ########## # ##### # ## # # # ## # ## # # # ## # # # # # # ## ### ## # # # # # ### # #### ## # # # # # # # # # # # #### ### ## # # # # # # # ## # ### # # # # # # # ## # ## # # # ## # ### # # # ### # # # # # ### # # # # ######## # ## # # # ## Fortaleza Maceió Camocim Caiçara do Norte Baia Formosa Tamandaré Recife Salvador
Brazil
Atlantic Ocean# # # # # # # # 0 100 200 300 400 500 600 Miles Land 1,000 m Isobath # Fishing areas N E W S 12° 12° 10° 10° 8° 8° 6° 6° 4° 4° 2° 2° 42° 42° 40° 40° 38° 38° 36° 36° 34° 34° 32° 32°
Table 1. General results of the general linear model analysis for the catch probability (binomial model and logistic link function) of yellowtail snapper, Lutjanus chrysurus.
95% confidence interval
Parameter B Std. Error Lower Upper P
Intercept -3.7880 0.7092 -5.1781 -2.3980 0.0000 Boat (motor) -0.9024 0.2224 -1.3383 -0.4665 0.0000 Boat (sail) 0.1062 0.4574 -0.7903 1.0027 0.8164 Canoe (sail) 0.3757 0.8067 -1.2054 1.9569 0.6414 Raft (sail) 2.3939 0.4818 1.4495 3.3383 0.0000 Launch (motor) 1.1791 2.7876 -4.2846 6.6428 0.6723
Small schooner (motor) - - - - -
Fisheries period (both) 0.0648 0.1789 -0.2858 0.4154 0.7173 Fisheries period (day) -1.0295 0.2119 -1.4448 -0.6142 0.0000
Fisheries period (night) - - - - -
Depth -0.0180 0.0023 -0.0225 -0.0135 0.0000
Latitude -0.3952 0.0496 -0.4923 -0.2981 0.0000
Distance from the coast 0.0355 0.0088 0.0182 0.0528 0.0001
Month 0.0781 0.0171 0.0446 0.1117 0.0000
Boat (motor) × effort 0.0184 0.0055 0.0077 0.0291 0.0007 Boat (sail) × effort 0.0135 0.0053 0.0030 0.0239 0.0114 Canoe (sail) × effort 0.0332 0.0155 0.0028 0.0637 0.0324 Raft (sail) × effort -0.0109 0.0077 -0.0260 0.0042 0.1577 Launch (motor) × effort -0.6144 0.7462 -2.0769 0.8481 0.4103 Small schhooner (motor) × effort -0.0097 0.0085 -0.0264 0.0069 0.2527
Table 2. General results of the general linear model analysis for the expected catch weight of yellowtail snapper, Lutjanus chrysurus, for positive deployments (gamma model and identity link function).
Table 3. Summary of regressions tested for relative-abundance index and latitude to establish the latitudinal model for the yellowtail snapper, Lutjanus chrysurus, in northeastern Brazil.
Model R2 F df1 df2 P Constant b 1 b2 b3 Linear 0.479 1 428 1 1 554 0 -1.235 -0.671 Quadratic 0.611 1 217 2 1 553 0 5.771 1.404 0.124 Cubic 0.208 873 3 1 552 0 -0.677 -1.872 -0.337 -0.019 95% confidence interval
Parameter B Std. Error Lower Upper P
Intercept 3 186 881 1 459 4 914 0.0003 Boat (motor) 0.0584 1.3419 -2.5718 2.6885 0.9653 Boat (sail) 11.3716 1.5244 8.3838 14.3594 0.0000 Canoe (sail) 22.6387 7.0350 8.8505 36.4270 0.0013 Raft (sail) 16.2663 1.6693 12.9946 19.5380 0.0000 Launch (motor) 6.1995 9.1121 -11.6600 24.0590 0.4963
Small schooner (motor) - - - - -
Fisheries period (both) -1.7768 1.3349 -4.3932 0.8397 0.1832 Fisheries period (day) -10.6648 1.2683 -13.1506 -8.1791 0.0000
Fisheries period (night) - - - - -
Depth 0.0550 0.0186 0.0184 0.0915 0.0032
Latitude -2.3570 0.1816 -2.7129 -2.0011 0.0000
Year -1.6034 0.4407 -2.4672 -0.7396 0.0003
Boat (motor) × effort 0.1404 0.0387 0.0646 0.2162 0.0003 Boat (sail)*effort 0.3522 0.0842 0.1873 0.5171 0.0000 Canoe (sail)*effort 0.1104 0.1353 -0.1548 0.3757 0.4146 Raft (sail)*effort -0.0424 0.0140 -0.0698 -0.0150 0.0024 Launch (motor)*effort 0.0368 0.0334 -0.0287 0.1022 0.2712 Small schhooner (motor)*effort 0.0117 0.0615 -0.1088 0.1323 0.8486
0 2 4 6 8 10 12 14 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CPU E ( K g-ho ok l-d ay ) Latitude CPUE Linear model Quadratic model Cubic model a
(a)
0 1 2 3 4 5 6 7 8 9 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CPU E ( K g-hook -day ) Latitude Cubic model(b)
Figure 2. (a) Linear, cubic and quadratic models tested for relative-abundance index (CPUE) and latitude
(b) Cubic latitudinal model adjusted to CPUE and latitude, for the yellowtail snapper, Lutjanus chrysurus, in northeastern Brazil.
-1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 50 100 150 200 250 300 350 CP UE (K g/ho o k /d ay ) Depth (m) CPUE Quadratic model Cubic model Nonlinear model
(a)
0 1 2 3 4 5 6 7 8 -50 0 50 100 150 200 250 300 350 CP U E (K g/h o o k /d a y ) Depth (m)(b)
Figure 3. (a) Quadratic, cubic and nonlinear models tested for relative-abundance index (CPUE) and depth
(b) nonlinear bathymetric model adjusted to CPUE and depth, for the yellowtail snapper, Lutjanus chrysurus, in northeastern Brazil.
Table 4. Summary of regressions tested for relative-abundance index and depth to establish the bathymetric model for the yellowtail snapper, Lutjanus chrysurus, in northeastern Brazil.
Model R2 F df1 df2 P Constant b1 b 2 b3
Linear 0.2 388.16 1 1 554 0 8.422 -0.047
Quadratic 0.207 202.81 2 1 553 0 9.321 -0.071 0.00013
Cubic 0.208 135.9 3 1 552 0 8.672 -0.049 -0.000077 0.00000048
The non-linear regression performed to establish the geographic model fit the data well (R2 = 0.754; P < 0.001) and reasonably described the mean CPUE trends in relation to latitude and depth. The mean trends of the geographic model with regard to depth and latitude are displayed in Figure 4a and 4b. 0 2 4 6 8 10 12 14 0 50 100 150 200 250 300 350 Depth (m) C P U E ( K g/ ho ok /d a y) Observed CPUE Mean CPUE Geografic model n=1 556 a (a) 0 2 4 6 8 10 12 14 -14 -12 -10 -8 -6 -4 -2 0 Latitude CP U E (K g/ hook /day ) Observed CPUE Mean CPUE Geografic model n=1 556 b (b)
Figure 4. Mean values of relative-abundance index (CPUE) and geographic model estimated for the yellowtail snapper, Lutjanus chrysurus, in relation to (a) depth and (b) latitude.
Applying map-algebra methods with the coefficients of the estimated models, maps of the expected CPUE index are obtained using GIS. The maps corresponding to the considered period are represented in Maps 2 and 3. Salvador Aracajú Maceió Recife Natal Fortaleza Camocim João Pessoa Piaui Ceará Rio Grande do Norte Paraíba Pernambuco Alagoas Sergipe Bahia Atlantic Ocean
Brasil
0 200 400 600 800 Kilometers CPUE (Kg/hook/day) 0.1 - 0.8 0.8 - 1.6 1.6 - 2.4 2.4 - 3.2 3.2 - 3.9 3.9 - 4.7 4.7 - 5.5 5.5 - 6.3 6.3 - 7.1 No Data 1000 m Isobath 200 m Isobath 50 m Isobath Land N E W S 12° 12° 10° 10° 8° 8° 6° 6° 4° 4° 2° 2° 42° 42° 40° 40° 38° 38° 36° 36° 34° 34° 32° 32°Map 2. Map of the estimated bathymetric model of yellowtail snapper, Lutjanus chrysurus. The map is represented in Mercator projection, relative-abundance index (CPUE) expressed in kg/hook/day, and longitude and latitude in degrees.
Salvador Aracajú Maceió Recife Natal Fortaleza Camocim João Pessoa Piaui Ceará Rio Grande do Norte Paraíba Pernambuco Alagoas Sergipe Bahia Atlantic Ocean
Brazil
0 200 400 600 800 Kilometers CPUE (Kg/hook/day) 0.1 - 1.1 1.1 - 2.2 2.2 - 3.3 3.3 - 4.3 4.3 - 5.4 5.4 - 6.5 6.5 - 7.6 7.6 - 8.7 8.7 - 9.8 No Data 1000 m Isobath 200 m Isobath 50 m Isobath Land N E W S 12° 12° 10° 10° 8° 8° 6° 6° 4° 4° 2° 2° 42° 42° 40° 40° 38° 38° 36° 36° 34° 34° 32° 32°Map 3. Map of the estimated geographic model of yellowtail snapper, Lutjanus chrysurus. The map is represented in Mercator projection, relative-abundance index (CPUE) expressed in kg/hook/day, and longitude and latitude in degrees.
4. Discussion
The standardization of the CPUE index using GLMs improved the volume of information in the analyses, especially the inclusion of fishing trips on which L. chrysurus was not caught as well as the effects of time, space and fishing fleet. There is a worldwide trend toward standardizing abundance indices of catch and effort data (Helser et al., 2004; Maunder and Punt 2004; Nishida and Chen 2004; Venables and Dichmont 2004; Xiao 2004).
With the help of spatial statistics it was possible to identify latitudinal and bathymetric trends in abundance of yellowtail snapper in the northeast of Brazil. In the southern study area higher levels of abundance were estimated. The species is also very important for the fleet of the central coast of Brazil (13°S to 23°S, i.e. from Salvador towards Rio de Janeiro), which represented 25% of the total landed by this fleet between 1997 and 2000 (Costa et al., 2005).
Based on the bathymetric and geographic models, mean abundance indexes were greater between depths of 25 and 50 m. Costa
et al. (2005) estimated that the average greatest abundance of L. chrysurus on the central coast of Brazil was between 20 and 60 m depth,
consistent with the results obtained in this study. Paiva and Fontelles-Filho (1995) also reported yields of demersal fish to be larger between 31 and 60 m deep in the Abrolhos area, similar to the depths where L. chrysurus had the greatest abundance in northeastern Brazil.
The resource studied displays a preference for depths between 25 and 50 m, probably because these depths offer favorable environmental factors for maintenance of the population. According to Helfman et al. (1997), the distribution of stocks is limited by ecological factors, such as nutrients and the topology of the habitats, which species choose based on characteristics of feeding, predation and breeding.
Areas with depths ranging from 25 to 50 m off northeastern Brazil constitute an essential environment for the maintenance of the yellowtail snapper stock; thus, such areas should be prioritized for the conservation and management of the resource in the region. The concept of essential fish environments is currently widely discussed by authors who include the spatial modeling of resources in stock assessments (Booth, 1998; Fisher et al., 2000; Ross and Ott, 2000).
The use of spatial analysis, technical development and subsequent geostatistical mapping using GIS to identify trends of mean abundance of L. chrysurus should contribute to a better understanding of the correlation between the distribution and abundance of this resource in the region. The analytical and functional possibilities offered by GIS to allow improved visualization facilitate the investigation of dynamic space–time, associated with fish, fisheries and their ecosystems (Nishida & Boot, 2001).
Acknowledgments
The authors would like to thank the fishing communities of northeastern Brazil; the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior for the research grant (Process: 5196/06-0) that facilitated the cooperation between the research centers in Brazil (Universidade Federal de Rio Grande) and Spain (Universidade de Alicante); and the Conselho Nacional de Desenvolvimento Científico e Tecnológico for the research productivity grant (Process: 301048/83-OC). The present study was financed by the Ministry of the Environment and Secretary of the Inter-Ministerial Commission for Ocean Resources, within the scope of the National Assessment Program for the Sustainable Potential of Living Resources in the Exclusive Economic Zone of Brazil.
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