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Latin american journal of aquatic research

On-line version ISSN 0718-560X

Lat. Am. J. Aquat. Res. vol.44 no.5 Valparaíso Nov. 2016 


Research Article


Spatial management units for industrial demersal fisheries in Southeastern and southern Brazil

Unidades de manejo espacial para las pesquerías demersales de la región sureste y sur de Brasil


Ana Paula Rosso1 & Paulo Ricardo Pezzuto1

1 Universidade do Vale do Itajaí, Centro de Ciências Tecnológicas da Terra e do Mar Itajaí, Santa Catarina, Brazil

Corresponding author: Paulo Ricardo Pezzuto (
Corresponding editor: Dante Queirolo

ABSTRACT. Most fishing management systems in the world have long assessed and managed single-stocks over very large areas. The increasing use of the oceans and the problems faced by many fisheries have increased the trend to focus on spatial-based fisheries management. Most of the Brazilian industrial fisheries concentrate in the so-called "southeastern-south region" (SE/S), a large jurisdictional area exceeding 1600 km of latitudinal extent, along which industrial demersal fishing vessels operate quite unrestrictedly, resulting in large overlapping in their catch composition and fishing areas. Despite being essentially multi-specific, management of these fisheries is focused on a few target-species, with poor results in terms of sustainability. This paper aimed at identifying possible future spatial fishing management units for the region, based on the distribution of demersal stocks, depth, bottom characteristics and fishing dynamics from double-rig, pair and stern trawlers, bottom gillneters, bottom longliners and octopus potters which were landed in Santa Catarina harbors (southern Brazil) between 2010 and 2012. Based on these data and information from existing literature, six potential spatial units were proposed at coastal (<30 m depth), shelf (southeastern and southern sectors) (30 to 100 m), shelf break (100 to 250 m) and slope areas (>250 m). This new scheme represents a step forward in designing a final spatial management model for the SE/S industrial fisheries, improving a previous model published in the early 2000' s and conforming, in general, to the three main subdivisions recognized for the South Brazil Large Marine Ecosystem.

Keywords: fishing management, marine spatial planning, demersal fisheries, industrial fisheries, ecosystem approach in fisheries, southeastern-south Brazil.

RESUMEN. En el mundo la mayoría de los sistemas de manejo de pesquerías han evaluado y manejado stocks únicos en extensas áreas. El aumento del uso de los océanos y los problemas enfrentados por muchas pesquerías han generado un enfoque creciente de atención en el manejo pesquero basado en áreas. La mayor parte de las pesquerías industriales brasileñas se concentra en la llamada "región sureste y sur" (SE/S), una gran área jurisdiccional que excede 1600 km de extensión latitudinal, donde los buques pesqueros involucrados en las pesquerías demersales industriales operan casi sin restricción, resultando en una gran superposición en la composición de capturas y áreas de pesca. Al ser éstas esencialmente multi-específicas, el manejo de estas pesquerías se basa en un grupo pequeño de especies objetivo con resultados pobres en términos de sustentabilidad. El artículo busca identificar posibles unidades de manejo pesquero espacial para la región, sobre la base de la distribución de los stocks demersales, profundidad y tipos de fondo, así como también en la dinámica de las flotas de arrastre dobles, arrastre simple, arrastre por parejas, enmalle de fondo, palangre de fondo y trampas para pulpo, las que desembarcaron sus capturas en los puertos de Santa Catarina (sur de Brasil) entre 2010 y 2012. Basado en estos datos y en información obtenida en la literatura, se propusieron seis unidades espaciales de manejo en la zona costera (<30 m de profundidad), plataforma continental (sectores sur y sureste, 100-250 m) y talud continental (>250 m). Este nuevo esquema representa un avance en el desarrollo de un modelo de manejo espacial para las pesquerías industriales del SE/S, mejorando un modelo anterior publicado a inicios del 2000 y se ajusta en general, a las tres principales subdivisiones reconocidas para el Gran Ecosistema Marino del Sur de Brasil.

Palabras clave: manejo pesquero, planificación espacial marina, pesquerías demersales, pesquerías industriales, enfoque ecosistémico en la pesca, sureste y sur de Brasil.



For a long time, most fishing management systems in the world has assessed and managed single-stocks over very large areas (Palumbi, 2004; Wilen, 2004; Claudet et al., 2006). The boundaries between these juris-dictional zones were frequently defined with no correlation with biological, oceanographic and/or human use patterns, mainly due to the scarcity of meaningful information available to the managers at the time of their definition (Wilen, 2004; Norse, 2010). The combination of the structure and processes of the biological components (and the respective environmental drivers) with management actions, has been viewed as a prerequisite for the sustainable management (Reiss et al, 2009, 2010).

This is especially true when moving from a single-species to an ecosystem approach in fisheries, in which a coherent space delimitation is of paramount importance. The increasing uses of the ocean and the problems faced by many fisheries (Norse, 2010; Jentoft & Knol, 2014), have increased the focus on spatial-based fisheries management, including, the formulation and enforcement of no-take areas, spatial zoning of fleet access, spatial restrictions of gear use and spatial user rights and/or catch quotas (Wilen, 2004; Pipitone, 2012; Rassweiler et al., 2012). In general, such strategies restrict the unlimited mobility of fishermen, a feature that fosters sequential overfishing (Berkes et al., 2006; Norse, 2010), and indiscriminate habitat exploitation (Bax et al., 1999).

Most of the Brazilian industrial fisheries concentrate in the so-called "Southeastern-South region" (hereafter named SE/S region), a large management area exceeding 1600 km of latitudinal extent, ranging from 18o20'S to the border with Uruguay. In general, vessels are authorized to operate along this entire region, eventually, with some bathymetric restrictions depending on the specific license. Besides the quite unrestricted spatial regime, management of the demersal fisheries conducted in this area has traditionally focused on a few (generally overexploited) target-species, in spite of most of these fisheries are highly multi-specific (Pezzuto & Benincá, 2015). This scenario has produced a substantial overlapping in catch composition and fishing areas exploited by hundreds of vessels (Perez et al., 2001; Valentini & Pezzuto, 2006; Kolling et al., 2008; Imoto, 2014; Pezzuto & Benincá, 2015; Dias & Perez, 2016; Pio et al., 2016) leaving fishing resources and ecosystem at risk.

In the last 15 years, several authors have pointed out the need of changing the management of demersal fisheries in SE/S Brazil from a species-based to a spatial-based approach (e.g., Perez et al., 2001; Okubo-da-Silva, 2007; Kolling et al, 2008; Pezzuto & Benincá, 2015). Under this approach, the region should be divided in smaller spatial management units, designed according to the respective biological assemblages (including the different stocks), bottom characteristics, depth, fleet dynamics and technical considerations. Fleets should only be allowed to operate inside one or a few units, following specific management measures.

Regarding bottom trawl and gillnet fisheries, Perez et al. (2001) published a first formal proposal in this direction, suggesting the subdivision of the SE/S region in six new management units, bordered by the latitudes 22o52'S and 29o20'S and the 100 m isobath. In the present paper we expand this proposal, by: a) adding bottom longliners and octopus potters to the analyzed fleets, b) including bottom characteristics as a new variable, c) considering recent data reflecting the expansion of the regional industrial fisheries to the slope, as occurred mostly after the late 1990's and early 2000's (see review in Perez et al., 2009), and d) improving the identification of management units through a multivariate approach.


Fishing and bottom data

Fishing data were provided by the Santa Catarina Industrial Fishing Statistical Program, developed by UNIVALI (University of Vale do Itajaí) since 2000. Effort, fishing areas and landings per "species" were collected through skippers' interviews, logbooks and/or sales records from 5,977 fishing trips conducted by industrial double-rig, pair and stern trawlers, bottom gillnetters, bottom longliners and octopus potters that landed in Santa Catarina harbors between January 2010 and December 2012. "Species" here refers to the commercial designations used by the local industry to identify fishing products. A "species" may correspond, therefore, to a single biological species (not necessarily from a unique stock), or to a complex of two or more biological species, eventually pertaining to different genera or families.

The data used to characterization benthic habitat were derived from a database structured in GIS (Geographic Information System), accumulated between 1970 and 2002 and provided by the CPRM - Geological Survey of Brazil (Companhia de Pesquisa de Recursos Minerais) (Bizzi et al., 2003).

Identification of main fishing grounds

The 35 most important demersal species landed in Santa Catarina were selected for this study (Table 1). They were chosen by identifying, for each fleet, the items that contributed cumulatively to 95% of the total biomass landed during the study period. Following the procedures adopted by the Fishing Statistical Program, the study area was divided into geographic grid squares of 30'x30' of resolution. For each fleet, the total number of trips recorded in the period in each grid square was calculated. Several grid squares were frequently visited by each vessel during a single trip. Considering that catch information is recorded per trip and not per tow, the presence of determined species in the landings was interpreted as resulting from catches obtained in all grid squares exploited by a vessel in a single trip. This procedure necessarily produces some biases in the data; for instance, coastal resources may appear as been caught also in the slope in case of a vessel exploiting both areas during a single trip. In order to overcome this problem, the frequency of occurrence of each species was calculated per grid square considering all trips of all fleets, and the respective 40 percentile was determined. Grid squares where the frequency of a species was lower than the respective 40 percentile, had its frequency changed to zero, emphasizing therefore, the identification of the main "core" areas in the species distribution.


Table 1. Vernacular and scientific names of the demersal fishery resources analyzed in this
study. *Not included in the multivariate analysis.


Cluster analysis (Clarke & Warwick, 1994) was used to identify fishing grounds according to the respective species associations. The data matrix of the frequency of occurrence of the several species (considering all trips of all vessels) in the corresponding grid squares was standardized by the total values, and the Bray-Curtis index was chosen to measure the similarity between the objects (i.e., grid squares). Grouping was performed by using the group average method. Grid squares not visited by the fleets during the study period were eliminated from the analysis. A SIMPER (Similarity Percentage) analysis was used in order to identify the main species contributing to each group (i.e., fishing ground) (Clarke, 1993).

The Cluster and SIMPER analysis were carried out by R computational program using the vegan package (R Development Core Team, 2015) and software PRIMER 7.0.10 (Plymouth Routines in Multivariate Ecological Research) (trial version) (Clarke & Gorley, 2015), respectively.

Spatial distribution of fishing effort and species

Maps representing the spatial distribution of the fishing effort (i.e., total number of trips recorded per grid square between 2010 and 2012) were produced for each fleet using Spatial Join tool in ESRI® ArcGIS™ software. The respective percentage of trips (and biomass landed) recorded in each fishing ground was then calculated as well as the relative frequency of occurrence and the relative contribution of each group to the total biomass landed of each species. Both results were organized in Tables.

Benthic habitat characterization

Bottom samples were originally classified in the CPRM database in 11 qualitative categories, which were secondarily grouped in the present study as: calcareous algae; sand and biodetritic gravel (including the original sand/biodetritic gravel and biodetritic/mud); muddy-sand; sand (including fine, medium and coarse sand); mud; gravel (including gravel and shells) and reef. The original geographical coordinates of each bottom sample were centered in the respective grid square. The percentage of occurrence of each bottom type was calculated for each group (fishing ground), in order to describe its main bottom characteristics, using Spatial Join tool in ESRI® ArcGIS™ software.

In addition, each geographic grid square was classified semi-quantitatively in five depth levels. The classification was based on Perez & Pezzuto (2006) and Haimovici et al. (1994), with small modifications. The following levels were defined: a) Level 1: coastal areas from 0 to 30 m depth; b) Level 2: inner continental shelf (30 to 100 m); c) Level 3: shelf break (100 to 250 m); d) Level 4: upper slope (250 to 500 m); e) Level 5: lower slope (depths exceeding 500 m). Grid squares covering more than a single bathymetric stratum were assigned to the stratum presenting the largest area.

Relationship between species and environmental variables

The relationship between the distribution of the 35 species (response variables) and depth, latitude and bottom types (explicative variables) was investigated by using Canonical Correspondence Analysis (CCA) (Ter Braak & Smilauer, 2002). The inclusion of the environmental variables in the model was performed through a forward selection procedure followed by a significance test (P < 0.05) conducted after 499 Monte Carlo permutations. Only depth, latitude, sand, gravel and calcareous algae bottom types were retained in the analysis.

This analysis was carried out by R computational program using the vegan package (R Development Core Team, 2015).


Identification of main fishing grounds

At a 13% level of similarity, the cluster analysis identified three main faunal components, which reflected the major bathymetric domains of the study area: a) coastal; b) shelf and c) slope grounds (Fig. 1).


Figure 1. Cluster analysis results obtained from the grid squares of the similarity of the total
occurrence of demersal resources (absolute frequency), considering together all fleets and
the entire study period. Highlighted the cutoff level of 57% similarity used to define the groups
and subgroups of grid squares (the latter indicated by the letters).


At a higher similarity level however (57%), six new spatially consistent groups emerged (Fig. 2):


Figure 2. Geographic distribution of the groups and subgroups of grid squares (i.e., geographical
areas of 30,x30,resolution) as resulting from the cluster analysis, based on the similarity in the
composition of the respective demersal fishing resources.


Northern Group: including the grid squares situated at the northern limit of the study area (off Rio de Janeiro State) and extending from the coast to the shelf break. The group was composed by subgroups "B" and "C" (Fig. 2). Pink-shrimp was the single species contributing to subgroup "B" (96%), while the subgroup "C" consisted of five species (Table 2).


Table 2. Results of SIMPER analysis with group, subgroup, name and percentage of each
species composing the subgroups. Subg. = Subgroups; Contrib. (%) = Percentage of
contribution; NICS: Northern Inner Continental Shelf; SICN: Southern Inner Continental Shelf.


Coastal Group: including subgroups "D", "E" and "F", ranged from northern São Paulo (23°30'S, 45°30'W) to center Paraná (25°00'S, 48°30'W) in areas shallower than 25 m depth (Fig. 2). Only sea-bob shrimp contributed to this group (93%) (Table 2), with an average similarity of 78.4%.

Northern Inner Continental Shelf: it was formed by subgroups "G" (a single grid square) and "H', extending mostly between 25 and 100 m depth from central Santa Catarina State (27°S) to southern Rio de Janeiro (23°S) (Fig. 2). Sixteen species contributed to the group (Table 2).

Southern Inner Continental Shelf: composed by subgroup "I". It is located southward from 27°S, mostly from the coast to 100 m depth (Fig. 2). The SIMPER analysis showed that 16 resources contributed to this group (Table 2).

Shelf Break: formed only by subgroup "J" which includes a series of contiguous grid squares situated southern from 28°S, between 100 and 250 m depth, and some other isolated areas found in northernmost areas (Fig. 2). Showing an average similarity of 83.5%, sixteen species were reported contributing to this group (Table 2).

Slope: four subgroups were enclosed in the slope group ("L", "M", "N" and "O"), corresponding to fishing grounds deeper than 100 m in the northern and 200 m in the southern portions of the study area (Fig. 2). According to SIMPER, five different species contributed to the whole group (Table 2). As subgroup "N" was represented by a single grid square (Fig. 2), SIMPER analysis was not able to identify any species contributing to its formation (Table 2).

Subgroups "A" and "K" (1 and 3 grid squares, respectively) (Fig. 2) located respectively in coastal areas of Santa Catarina and Rio Grande do Sul, were not associated to any particular major group. The latter was formed by nine species (Table 2).

Spatial distribution of fishing fleets

Most of the trips conducted by the demersal fleets operating from Santa Catarina harbors were carried out in areas corresponding to the inner continental shelf, shelf break and slope (Table 3). Accordingly, the same areas produced the major part of the landings in biomass (Table 4). Operations of the double-rig trawlers and bottom gillnetters exhibited the broadest distribution of trips along the study area, in spite of the latter concentrated more than 50% of their trips in the northern inner continental shelf. Pair and stern trawlers, otherwise, operated mostly in the southern shelf. Effort from bottom longliners were nearly equally spreaded in this same area, the shelf break and slope. Finally, more than 90% of the trips conducted by vessels targeting octopus occurred in the inner continental shelf (Table 3, Fig. 3). The area corresponding to the southern inner continental shelf was the most important in terms of biomass landed for all fleets, excepting for the bottom gillnetters, whose landings were similar in the northern inner continental shelf and southern inner continental shelf (Table 4).


Table 3. Percentage of the total number of trips carried out by the industrial demersal fleets
landing in Santa Catarina between 2010 and 2012, broken down into groups and subgroups
identified in the cluster analysis. DT: double-rig trawl, PT: pair trawl, ST: stern trawl, BL:
bottom longline, BG: bottom gillnet, OP: octopus pot, NICS: northern inner continental shelf,
SICS: southern inner continental shelf.


Table 4. Percentage of the landings (kg) recorded by the industrial demersal fleets landing
in Santa Catarina between 2010 and 2012, broken down into groups and subgroups identified
in the cluster analysis. DT: double-rig trawl, PT: pair trawl, ST: stern trawl, BL: bottom
longline, BG: bottom gillnet, OP: octopus pot, NICS: northern inner continental shelf, SICS:
southern inner continental shelf.


Figure 3. Spatial distribution of the industrial demersal fleets landing in Santa Catarina between
2010 and 2012. Values are number of trips recorded in geographical areas of 30'x30' resolution.
ES: Espírito Santo, RJ: Rio de Janeiro, SP: São Paulo, PR: Paraná, SC: Santa Catarina, RS: Rio
Grande do Sul.


Spatial distribution of the demersal resources

As the use of the northern area by the fleets was only marginal (Table 3), resources caught in this area showed both small frequency of occurrence (Table 5) and low contribution to the total landings (Table 6). An exception was the striped soldier shrimp landings, which represented 37.0% (Table 6). A similar pattern was observed in the coastal area, where sea-bob shrimp was by far the main species either in terms of frequency of occurrence and in biomass landed (Tables 5 and 6).


Table 5. Frequency of occurrence (%) of the main 35 demersal resources exploited by the
industrial fleets in the SE/S region, per group and subgroup identified in the cluster analysis.
NICS: northern inner continental shelf, SICS: southern inner continental shelf.


Table 6. Percentage of landings (kg) of the main 35 demersal resources exploited by the
industrial fleets in the SE/S region, per group and subgroup identified in the cluster analysis.
NICS: northern inner continental shelf, SICS: southern inner continental shelf.


The inner continental shelf and the shelf break showed the highest diversity, as all 35 species were recorded in trips conducted along these areas, many of them showing high frequency of occurrence (Table 5) and/or high participation in the total biomass landed (Table 6). In the southern inner continental shelf, 20 of the 35 selected resources revealed contributions in the landings from 58.4 to 97.7%, denoting the high importance of the area to the Santa Catarina fleets. More than 95% of the landings of acoupa weakfish, sea trout and Argentine red and Argentine stiletto shrimps came from this area (Table 6). On the other hand, landings of pink-shrimp (80.4%), sea-bob shrimp (78.1%) and squid (83.0%) came mainly from the northern inner continental shelf (Table 6). Tomtate grunt and whitemouth croaker showed a quite balanced production in both areas (Table 6). In general, resources caught in the northern inner continental shelf showed a higher importance in terms of frequency of occurrence than in terms of biomass landed, probably reflecting an unequal biological productivity between those areas. For instance, frequencies of catfish in the northern and southern inner continental shelf represented respectively, 50.9% and 38.1%, while their biomass landed showed an inverse pattern, totaling 10.8% and 58.4% in the same areas (Tables 5 and 6).

Castaneta, codling and pink cusk-eel were the most frequent resources in the shelf break (Table 5).

However, Argentine croaker, Argentine hake, bluewing searobin, catfish, monkfish, red porgy, sand flounder added to the three former species were the most important resources in terms of biomass landed (Table 6). Argentine hake, codling, monkfish, pink cusk-eel, sandperch and tilefish were caught mostly in slope grounds (Table 6), in spite of several of these species had exhibited high frequencies of occurrence out of this area (Table 5).

Characterization of benthic environment

A minimum of one and a maximum of 1,336 sediment samples were available for each of the 199 grid squares of the study area. About 60% of them presented more than 30 samples. Sand, muddy-sand and mud were much more frequent than reefs, gravel, sand with biodetritic gravel and calcareous algae (Table 7).


Table 7. Percentage of occurrence of the bottom types in the subgroups
identified in the cluster analysis. NICS: northern inner continental shelf, SICS:
southern inner continental shelf.


In general, samples classified as gravel and sand with biodetritic gravel were more frequent in the northern and slope areas. Muddy bottoms showed higher frequencies in the shelf break and slope grounds, although it also appeared in many sediment samples from the inner continental shelf. Calcareous algae and reefs occurred punctually in the northern, coastal and slope grounds. Sand and muddy-sand samples were largely distributed along the whole study area (Table 7).

Relationship between demersal resources and environmental variables

High correlations between species distribution and environment variables (latitude, depth and sand, gravel and calcareous algae bottom types) were found in the first and second axis (0.783 and 0.894, respectively) of the Canonical Correspondence Analysis (CCA). The former axis was highly correlated with depth and the latter with latitude (Fig. 4).


Figure 4. Plot of the Canonical Correspondence Analysis results among depth,
latitude and bottom types (environmental variables) and demersal resources
distribution (response variables). Lat: latitude, CalAlgae: Calcareous algae,
AC: Argentine croaker, AH: Argentine hake, AmH: American harvestfish, AR:
Argentine red shrimp, AS: Argentine stiletto shrimp, AW: acoupa weakfish,
BC: Brazilian codling, BF: Brazilian flathead, BS: bluewing searobin, C: codling,
CA: castaneta, CF: catfish, DG: dusky grouper, F: flounder, GT: grey triggerfish,
J: Jamaica weakfish, KW: king weakfish, LH: largehead hairtail, MF: monkfish,
O: octopus, PC: pink cusk-eel, PS: pink-shrimp, RP: red porgy, S: skate, SB:
sea-bob shrimp, SF: sand flounder, SK: southern kingcroaker, SP: sandperch,
SQ: squid, SS: striped soldier shrimp, ST: sea trout, SW: stripped weakfish,
TF: tilefish, TG: tomtate grunt, W: whitemouth croaker.


Codling and tilefish were positively correlated with depth, showing no clear association with latitude. Their occurrence probably is associated to gravelly or even muddy (a bottom type opposed to the sand along a granulometric gradient) bottoms (Fig. 4). Argentine hake, monkfish, pink cusk-eel, sandperch and striped soldier shrimp were also correlated with depth, but on a more intermediate position along the first axis. The latter species showed a clear trend towards a northern distribution while the other species were positioned near to the first axis, suggesting a wider latitudinal distribution. Harder bottoms (calcareous algae and gravel) and/or with low sand content (i.e., muddier) were mostly associated to these resources (Fig. 4).

Sea-bob shrimp, pink-shrimp, squid, catfish, American harvestfish, Jamaica weakfish, octopus, whitemouth croaker and bluewing searobin were negatively correlated with both depth and latitude (Fig. 4). Argentine red shrimp, Argentine stiletto shrimp, sea trout, acoupa weakfish, king weakfish and tomtate grunt were also negatively correlated with depth, but exhibited positive correlation with latitude (i.e., a southernmost distribution) (Fig. 4). In both cases, resources seemed to be related to sandy and/or muddy-sand bottoms (Fig. 4).

Fishing management units

The integration of biological, fishery and environmental data, either analyzed in the present study as available in the literature (see discussion), resulted in six main areas, from which future spatial fishing management units could evolve (Table 8, Fig. 5).


Table 8. Spatial management units proposed for the SE/S Brazil industrial demersal fisheries,
as resulting from data from the present study and information from the literature. * Values
are mean frequencies (%) of occurrence of the bottom types in the respective quadrants.
"Resources for which spatial regulations are enforced in the region.


Figure 5. Map of the spatial management units proposed for the SE/S Brazilian industrial
demersal fisheries, as resulting from data from the present study and information from the


North unit: comprises a small sand, muddy-sand and muddy area situated between southern and central parts of Rio de Janeiro state (23°45'-22°30'S). Octopus, pink-shrimp, striped soldier shrimp, codling and sandperch compose the main species assemblage of this unit, exploited mostly by double-rig trawlers and bottom gillnetters (Table 8, Fig. 5).

In addition, while pertaining to the SE/S region, grid squares between 22°30'S and 18oS were not included in the present analysis (and neither in the North unit), given the scarcity of fishing records available from the studied fleets.

Coastal unit: situated in shallow waters between 25°30'S (central Paraná) and 23°25'S (northern São Paulo) up to 30 m depth. This area is characterized by muddy-sand, muddy, and, in smaller proportions, by sandy bottoms, where the sea-bob shrimp fishery conducted by double-rig trawlers dominates (Table 8, Fig. 5). Given the limited availability of data concerning fishing operations conducted in shallow waters by the studied fleets, it is very likely that these latitudinal limits are artificially underrepresented.

Southeastern shelf unit (based on the northern inner continental shelf group): delimited by latitudes ~28°S (Cabo de Santa Marta; southern Santa Catarina) and ~23°S (Cabo Frio, southern Rio de Janeiro), from 30 to 100 m depth (Table 8, Fig. 5). Sand, mud and muddy-sands are the dominant bottoms (Table 8).

Whitemouth croaker (southeastern stock), squid, octopus, pink-shrimp, Brazilian codling, catfish, bluewing searobin, skate, American harvestfish, Jamaica weakfish e sand flounder compose the main species assemblage of this unit, sharing the last seven species with the Southern shelf unit. Octopus pot, double-rig trawl, bottom gillnet and pair trawl were the main fleets operating in the area (Table 8).

Southern shelf unit (based on the southern inner continental shelf group): extending from 28oS to the southern limit of the Brazilian Economic Exclusive Zone from the coast to 100 m depth (Fig. 5), the area is covered by mud, muddy-sand and sand, and is exploited mostly by trawlers (double-rig, pair and stern) and gillnetters. From the main 24 resources caught in the area, 17 are dominant (Table 8).

Shelf break unit: it was identified in the cluster analysis as a continuous area bordering only the southern continental shelf; however, this unit seems to extend uninterruptedly up to São Paulo state (23°25'S) in depths ranging from 100 to 250 m (Table 8, Fig. 5).

Muddy bottoms dominate in this sector, where bottom longliners, bottom gillnetters and double-rig and stern trawlers are the main fleets operating (Table 8). Sandperch, striped soldier shrimp, pink cusk-eel, bluewing searobin, skate, sand flounder and Brazilian codling were the main resources exploited in the shelf break. The last four species are shared with both southeastern and southern shelf units, while the pink cusk-eel occurs also in the slope (Table 8).

Slope unit: extends for areas exceeding 250 m depth situated from 23°35'S (northern São Paulo) to the southern limit of the Brazilian EEZ (Table 8, Fig. 5). Bottom types are quite heterogeneous in the area. Codling, tilefish, monkfish, Argentine hake and pink cusk-ell were the main slope resources identified in the present study (Table 8).


A critical point in tropical fisheries analysis as the one developed in the study area is their multispecific feature, and the fact that the same species is exploited by different fishing gears in distinct phases of its life cycle. Usually, a fleet explores several stocks, and several fleets compete by the same resources (Sparre & Venema, 1997). Under this scenario, the adoption of operational units, hereby called fishing management units, could be useful to reduce such complexity in the management process, through the identification of homogeneous groups of vessels associated with assemblages of resources and bottom types (Accadia & Franquesa, 2006).

Despite being considered a single jurisdictional area for fishing management purposes, the large SE/S region cannot be regarded as a homogeneous system deserving, therefore, a more regionalized approach. However, in determining the size of the management units, scale turns a fundamental issue. For fishing management purposes, excessively larger scales may turn management unsuccessful, as critical details may be lost, while excessively finer scales can make monitoring and enforcement impractical (Bax et al., 1999; Norse, 2010). When proposing new spatial management units for the SE/S region, we have adopted a megahabitat scale (100s of km) (Bax et al., 1999). This option was justified by: a) either the studied fleets as most of the exploited resources, are highly movable; b) the generally plain, soft-bottom and opened shelf is mostly submitted to large scale oceanographic processes; c) fishing data have a quite poor spatial resolution; and d) structural and operational limitations of the Brazilian fishing management authorities turn unfeasible dealing with an excessively large number of management units. Therefore, the six management areas defined in such geographic scale are discussed below.

North unit: includes either shallow as deep fishing grounds, resulting in a highly diverse assortment of species exploited, as the narrowing of the shelf in the region (Cooke et al., 2007) makes it possible for the vessels to reach great depths in a short navigation time. Therefore, in the same fishing trip the fleet is able to exploit either coastal species as deeper ones.

Bottom characteristics seem also to exert strong influence in the North unit design. Northern from Cabo Frio (~22°52'S) the shelf bottom is highly heterogeneous, being dominated by gravel, gravelly sand and calcareous algae, while sand areas remain confined to coastal areas (Martins et al., 1972; Figueiredo Jr. & Madureira, 2004; Figueiredo Jr. & Tessler, 2004). Most trawlable areas are therefore confined to southernmost latitudes, while passive gears tend to prevail over the more irregular bottoms predominating towards the north (Muehe & Garcez, 2005). In addition, while pertaining to the SE/S region, grid squares between 22°30'S and 18oS were not included in the present analysis (and neither in the North unit), given the scarcity of fishing records available from the studied fleets, probably resulting from the long distance to the Santa Catarina harbors. Consequently, this management area must be seen as provisional, depending on a reassessment based on fishing data from São Paulo and Rio de Janeiro fleets.

Coastal unit: given the limited availability of data concerning fishing operations conducted in shallow waters by the studied fleets, it is very likely that the latitudinal limits proposed for this small management unit are artificially underrepresented. In fact, extensive exploitation of sea-bob shrimp (the main species identified in the coastal unit) by double-rig trawlers is known to occur from Espírito Santo to southern Santa Catarina (Perez et al., 2001; Valentini & Pezzuto, 2006). Therefore, in the Perez et al. (2001) model a coastal (i.e., < 30 m depth) management sector extending along all this area was proposed. This should be considered in the future as more detailed data become available.

Southeastern shelf unit: it is inserted in the Southeastern Brazil Bight. The region is characterized by oceanographic processes such as coastal upwellings and sub superficial nutrient-rich cold water intrusions during the austral summer, the formation of meanders and eddies by the Brazil Current and the presence of relatively cold and less saline waters from the south during the winter (Castro et al., 2006; Rossi-Wongtschowski et al., 2006). The coastline is very indented in several sectors. Shelf width and depth gradient vary between 70 and 230 km and 1:600 to 1:1,300 respectively, and the shelf break lies on the 120 to 180 m isobaths (Corrêa & Villwock, 1996; Mahiques et al., 2010). Although the sand, mud and muddy-sands are the dominant bottoms, their distribution are not homogeneous in the area. Northward from 24°S there is a complex sedimentary mosaic, while southward the sediment distribution is comparatively more homogeneous (Mahiques et al., 2010).

Southern shelf unit: previously separated from the northern shelf unit by the cluster analysis in the parallel 27oS, the existence of either distinct oceanographic patterns as latitudinal changes in the structure of some stocks justify moving this limit to a southernmost position. Morphologically very homogeneous (Corrêa & Villwock, 1996), the southern shelf width varies from 100 to 200 km and its break lies between 100 and 180 m depth (Mahiques et al., 2010). Low gradients ranging from 1:600 to 1:1,000 are also typical in the area (Figueiredo Jr. & Tessler, 2004; Mahiques et al., 2010). The shelf is alternately influenced throughout the year by tropical (Brazil current) and sub-Antarctic (Malvinas current) waters flowing in opposite directions. In shallower sectors, they are also strongly influenced by low salinity and nutrient-rich waters originating from La Plata River and Patos Lagoon discharges (Castro et al., 2006). The seasonal transport of nutrient enriched waters increases the primary and secondary productivity in the region, showing a comparatively high fishing potential of demersal resources (Rossi-Wongtschowski et al., 2006; Haimovici et al., 2007). This fact explains, in part, the concentration of fishing effort (and catches) in this region, as showed by most of the studied fleets.

Despite the wide geographic distribution showed by most of the resources caught in this unit, their catches were more abundant in this area. In addition, there is evidence that the transition zone between the southeastern and southern shelf separates different stocks which have been identified at least for Argentine hake (Vaz-dos-Santos et al., 2009), king weakfish (Rodrigues et al., 2014), Argentine croaker (see references in Haimovici et al., 2006) and whitemouth croaker (Vazzoler, 1991; Vasconcellos et al., 2015).

This fact reinforces the need of considering the southeastern and southern areas as different management units.

Shelf break unit: originally identified in the cluster analysis as a discontinuous sector probably due to the methodological limitations related both to the coarse resolution of the fishery data and the high mobility of the fleets, this unit should instead to be considered as a continuous area extending along from 100 to 250 m along most of the study area. On the other hand, by sharing several species with other management units (Table 8), it probably constitutes a transitional area. Dominated by muddy bottoms, it is known that hard bottoms (not clearly identified in the present analysis by scale and data limitations) are concentrated also between 100 and 500 m in front of Santa Catarina State, potentially excluding the operation of trawlers in favor of longliners in some areas (Figueiredo Jr. & Tessler, 2004).

Slope unit: bottom types and morphology are quite heterogeneous in the area, influencing the operation of the different fishing fleets. Hence, while some areas have been regarded as non-trawlable due to the high slope gradient (1:13 to 1:8) and/or to the presence of hard bottoms, others have been successfully exploited by either double-rig as stern trawlers, given their gentler declivity and suiTable sedimentary coverage (1:132 to 1:190) (Figueiredo Jr. & Tessler, 2004).

Besides the species analyzed in the present study, deep-sea crabs (Chaceon spp.), deep-sea shrimps (Aristeidae) (Pezzuto et al., 2006; Dallagnolo et al., 2009), silver John dory (Zenopsis conchifera) and Argentine squid (Illex argentinus) (Perez et al., 2009) (Tables 1 and 8), whose landings were not available for the study period, should also be included among the slope resources.

Determined mostly from fishing-derived data, three of the six management units proposed in the present study have their boundaries conforming to the subdivisions of the so-called South Brazil Large Marine Ecosystem. Large Marine Ecosystems (LME) are "large marine areas of approximately 200,000 km2 or larger, adjacent to the continents in coastal waters where primary productivity is generally higher than in open ocean areas" (Sherman & Hempel, 2009). Based in four ecological criteria (i.e., bathymetry, hydrography, productivity and trophic relations), a total of 64 LMEs have been identified along the World Ocean, which account for nearly 80% of the world capture of marine fish (Sherman & Hempel, 2009). Following such criteria, three LMEs have been recognized along the Brazilian coast: a) North Brazil Shelf; b) East Brazil Shelf; and c) South Brazil Shelf (Sherman & Hempel, 2009). The latter LME extends from 22oS (Rio de Janeiro) to 34oS (Rio Grande do Sul states) (basically including the SE/S region) and has been subdivided in three smaller sub-areas as follows: a) Southeastern Bight (23°-28°S), characterized by seasonal upwellings and cold intrusions; b) South Shelf (28°-34°S), affected by estuarine outflows; and c) slope and oceanic system, influenced by mesoscale eddies (Gasalla, 2007 apud Heileman & Gasalla, 2009). As can be clearly observed, such sub-areas coincide precisely with the boundaries of the Southeastern shelf, Southern shelf and Slope spatial management units proposed in the present study, while the Shelf Break unit denotes a bathymetric transition among them. Produced by different approaches, such similarity points to the robustness of the divisions proposed here.

In general, the present findings corroborate also some aspects of the model from Perez et al. (2001) as: a) it identifies a coastal unit focused mainly on the sea-bob shrimp fishery; and b) it establishes two major latitudinal boundaries in the SE/S, despite some slight differences in their respective locations and respective bathymetric ranges. Otherwise, one of the most remarkable contrast between the two models, refers to the management of the outer shelf and slope areas. In the Perez et al. (2001) proposal, a single bathymetric limit separating shallower from deeper fisheries was established over the 100 m depth contour. Here we evidenced the need of a more detailed approach in the deeper areas, by recognizing two sectors (shelf break, from 100 to 250 m and slope, >250 m) distributed along the full latitudinal extent of the study area. Developing more significantly since late 1990's and early 2000's, deep-water fisheries were extensively monitored in the region during the period (see review by Perez et al., 2009) providing, in recent years, a much better understanding about resources distribution and respective potentials. Paradoxically, despite its recent history, efforts in assessment and management of deep-water fisheries in Brazil were comparatively more intense than to "traditional" coastal fisheries. Consequently, in some aspects the spatial approach considered here have already been implemented in the SE/S slope sector, where some fisheries have been space-regulated in terms of target-species, by-catches, TACs, effort limitations and technical measures (Brasil, 2008a, 2008b, 2008c, 2009). It is necessary to expand such approach to the shelf and coastal water demersal fisheries, for which several authors (e.g., Okubo-da-Silva, 2007; Imoto, 2014; Pezzuto & Benincá, 2015) have described spatial patterns that reinforce the need to change the present management scenario focused on target-species and unrestrictedly mobility.

The present study represents a step forward in designing a final spatial management model for the SE/S industrial fisheries, as could be evidenced, among other aspects, by the improvements over the previous model (i.e., Perez et al., 2001) and its correspondence with the South Brazil LME configuration. However, it presents some shortcomings that should be eliminated before a final design be accepted. For example, given the proximity of the isobaths either in the slope as in some parts of the shelf break, the coarse spatial resolution of the fishing data available, and the quite punctual distribution of coarse/consolidate bottoms in the region, relevant features in the mesohabitat scale (i.e., 10s km; sensu Bax et al., 1999) could not be identified in this present study. In addition, vessels landing in Santa Catarina are not fully representative of the fleets operating from other harbors of the SE/S, where technological and operational differences could produce different information about fishing strategies and catches.

Future refinements to the present model should benefit from considering other relevant biological, environmental, economic and social features regarding the regional fisheries and stocks. However, concerning fishing data, it should be considered, at least: a) landings from other harbors, for a better understanding of the fishing dynamics, especially in the northernmost areas; b) fine-scale satellite vessel monitoring system data (Jennings & Lee, 2012); and c) seasonal patterns in the fishery activity, as they could influence significantly the spatial effort allocation with time. In spite of these limitations, we expect that the present study can serve as a new base to subsidizing the future spatial planning of the fishing activity in the SE/S Brazil.


This study was funded by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Ministry of Education-Brazil) (Edital Capes Ciências do Mar 09/2009, AUXPE 1141/2010-IGEPESCA Project). A.P.R. was supported by a M.Sc. scholarship granted by CAPES/PROSUP. P.R.P. coordinates IGEPESCA/CAPES Project supported by a Research Grant from CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico - Ministry of Science, Technology and Innovation). The authors are indebted to the staff of the Group of Fishery Studies (GEP/UNIVALI) by providing the fishing data used in the present study and to the two anonymous referees for the comments that improved the manuscript.



Accadia, P. & R. Franquesa. 2006. The operational units approach for fisheries management in the Mediterranean Sea. SCSA/ SCSI/ SCESS Transversal Workshop on Stock Assessment and Operational Units, Rome, 40 pp.         [ Links ]

Bax, N., R. Kloser, A. Williams, T. Gowlett-Holmes & T. Ryan. 1999. Seafloor habitat definition for spatial management in fisheries: a case study on the continental shelf of southeast Australia. Oceanol. Acta, 22(6): 705-719.         [ Links ]

Berkes, F., T.P. Hughes, R. S. Steneck, J.A. Wilson, D.R. Bellwood, B. Crona, C. Folke, L.H. Gunderson, H.M. Leslie, J. Norberg, M. Nyström, P. Olsson, H. Österblom, M. Scheffer & B. Worm. 2006. Globalization, roving bandits, and marine resources. Science, 311: 1557-1558.         [ Links ]

Bizzi, L.A., C. Schobbenhaus, R.M. Vidotti & J.M. Gonçalves. 2003. Geologia tectónica e recursos minerais do Brasil: texto, mapas e SIG. CPRM - Serviço Geológico do Brasil, 692 pp.         [ Links ]

Brasil. 2008a. Instrução Normativa SEAP n° 22, de 1° de dezembro de 2008. Diário Oficial da União, Brasília, DF, n. 234, Seção 1, ISSN 1677-7042, pp. 4-5.         [ Links ]

Brasil. 2008b. Instrução Normativa SEAP, n° 21, de 1 de dezembro de 2008. Diário Oficial da União, Brasília, DF, n. 234, Seção 1, ISSN 1677-7042, pp. 3-4.         [ Links ]

Brasil. 2008c. Instrução Normativa SEAP, n° 23, de 4 de dezembro de 2008. Diário Oficial da União, Brasília, DF, n. 237, Seção 1, ISSN 1677-7042, pp. 3-4.         [ Links ]

Brasil. 2009. Instrução Normativa n° 03 (MPA/ MMA), de 4 de setembro de 2009. []. Reviewed: 20 October 2015.         [ Links ]

Castro, B.M., J.A. Lorenzzetti, I.C.A. Silveira & L.B. Miranda. 2006. Estrutura termohalina e circulação na região entre o Cabo de São Tomé (RJ) e o Chuí (RS). In: Rossi-Wongtschowski, C.L.D.B. & L.S.P. Madureira (eds.). O ambiente oceanográfico da plataforma continental e do talude na região sudeste-sul do Brasil. EDUSP, São Paulo, pp. 11-120.         [ Links ]

Clarke, K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian J. Ecol., 18: 117-143.         [ Links ]

Clarke, K.R. & R.N. Gorley. 2015. Getting started with PRIMER v7. PRIMER-E: Plymouth, Plymouth Marine Laboratory, 20 pp.         [ Links ]

Clarke, K.R. & R.M. Warwick. 1994. Change in marine communities: an approach to statistical analysis and interpretation. Plymouth Marine Laboratory, Plymouth, 144 pp.         [ Links ]

Claudet, J., S. Roussel, D. Pelletier & H. Rey-Valette. 2006. Spatial management of near shore coastal areas: the use of marine protected areas (MPAs) in a fisheries management context. Vie et Milieu - Life and Environment, 56(4): 301-305.         [ Links ]

Cooke, C.V., L.S.P. Madureira, H.H. Griep & M.P. Pinho. 2007. Análise de dados de ecossonda de fundo oriundos de cruzeiros realizados entre Fortaleza (CE) e Chuí (RS) com enfoque na morfologia e tipos de fundo. Rev. Bras. Geofísica, 25(4): 443-457.         [ Links ]

Corrêa, I.C.S. & J.A. Villwock. 1996. Continental shelf and coastal plain sediments of the Southeast and South coast of Brazil. In: Martins, L.R. & I.C.S. Corrêa (eds.). Morphology and sedimentology of the Southwest Atlantic coastal zone and continental shelf from Cabo Frio (Brazil) to Península Valdés (Argentina). Evangraf/ UFRGS-IG-CECO, Porto Alegre, 74 pp.         [ Links ]

Dallagnolo, R., J.A.A. Perez, P.R. Pezzuto & R. Wahrlich. 2009. The deep-sea shrimp fishery off Brazil (Decapoda: Aristeidae): development and present status. Lat. Am. J. Aquat. Res., 37(3): 327-346.         [ Links ]

Dias, M.C. & J.A.A. Perez. 2016. Multiple strategies developed by bottom trawlers to exploit fishing resources in deep areas off Brazil. Lat. Am. J. Aquat. Res., 44(5): 1055-1068.         [ Links ]

Figueiredo Jr., A.G. & L.S.P. Madureira. 2004. Topografia, composição, refletividade do substrato marinho e identificação de províncias sedimentares na região Sudeste-Sul do Brasil. Série documentos Revizee: Score Sul. Instituto Oceanográfico - USP, São Paulo, 64 pp.         [ Links ]

Figueiredo Jr., A.G. & M.G. Tessler. 2004. Topografia e composição do substrato marinho da região Sudeste-Sul do Brasil. Série documento Revizee: Score Sul. Instituto Oceanográfico - USP, São Paulo, 64 pp.         [ Links ]

Haimovici, M., L. Absalonsen, G. Velasco & L.W. Miranda. 2006. Diagnóstico do estoque e orientações para o ordenamento da pesca de Umbrina canosai (Berg, r 1895). In: C.L.D.B Rossi-Wongtschowski, A.O. Ávila-Da-Silva & M.C. Cergole (eds.). Análise das principais pescarias comerciais da região Sudeste-Sul do Brasil: dinâmica populacional das espécies em explotação II. Instituto Oceanográfico - USP, São Paulo, Série documentos Revizee: Score Sul, 96 pp.         [ Links ]

Haimovici, M., L.G. Fischer & L.V. Miranda. 2007. Densidades, biomassas e potencial pesqueiro de recursos demersais de fundos arrastáveis da plataforma continental do Brasil. In: M. Haimovici (org.). A prospecção pesqueira e abundância de estoques marinhos no Brasil nas décadas de 1960 a 1990: Levantamento de dados e Avaliação Crítica. 1ed. MMA/SMCQA, Brasília, 1: 181-191.         [ Links ]

Haimovici, M., A.S. Martins, J.L. Figueiredo & P.C. Vieira. 1994. Demersal bony fish of the outer shelf and upper slope of the southern Brazil subtropical convergence ecosystem. Mar. Ecol. Progr. Ser., 108(1/2): 59-77.         [ Links ]

Heileman, S. & M. Gasalla. 2009. XVI-54 South Brazil Shelf: LME #15. In: K. Sherman, & G. Hempel (eds.). The UNEP Ecosystem Report: a perspective on changing conditions in LMEs of the World's Regional Seas. UNEP Regional Seas Report and Studies n° 182. United Nations Environment Programme. Nairobi, Kenya, 2: 723-734.         [ Links ]

Imoto, R.D. 2014. A Dinâmica Espacial e Interação entre Frotas Pesqueiras na Bacia de Santos, Brasil. Dissertação Mestrado em Aquicultura e Pesca, Instituto de Pesca - APTA, São Paulo, 60 pp.         [ Links ]

Jennings, S. & J. Lee. 2012. Defining fishing grounds with vessel monitoring system data. ICES J. Mar. Sci., 69(1): 51-63.         [ Links ]

Jentoft, S. & M. Knol. 2014. Marine spatial planning: risk or opportunity for fisheries in the North Sea? Maritime Studies, 12: 13.         [ Links ]

Kolling, J.A., P.A. Batista, A.O. Ávila-Da-Silva & M.H. Carneiro. 2008. A utilização do ambiente marinho e de seus recursos vivos pela frota pesqueira paulista: o ambiente demersal. Série Relatórios Técnicos, São Paulo, 32: 1-62.         [ Links ]

Mahiques, M.M., S.H.M. Sousa, V.V. Furtado, M.G. Tessler, F.A.L. Toledo, L. Burone, R.C.L. Figueira, D.A. Klein, C.C. Martins & D.P.V. Alves. 2010. The Southern Brazilian shelf: general characteristics, quaternary evolution and sediment distribution. Braz. J. Oceanogr., 58(Special Issue PGGM): 25-34.         [ Links ]

Martins, L.R., J.A. Villwock & I.R. Martins. 1972. Estudo preliminar sobre a distribuição faciológica da plataforma continental brasileira. Pesquisas, 1: 51-56.         [ Links ]

Muehe, D. & D.S. Garcez. 2005. A plataforma continental brasileira em sua relação com a zona costeira e a pesca. Mercator - Revista de Geografia da UFC, (8): 69-88.         [ Links ]

Norse, E.A. 2010. Ecosystem-based spatial planning and management of marine fisheries: why and how? Bull. Mar. Sci., 86(2): 179-195.         [ Links ]

Okubo-da-Silva, S. 2007. Análise da estrutura da comunidade de peixes demersais da plataforma continental e talude superior do sudeste brasileiro vulneráveis à pesca de arrasto-de-fundo. Dissertação Mestrado em Aquicultura e Pesca, Instituto de Pesca -APTA, São Paulo, 105 pp.         [ Links ]

Palumbi, S.R. 2004. Marine reserves and ocean neighborhoods: the spatial scale of marine populations and their management. Ann. Rev. Env. Res., 29: 31-68.         [ Links ]

Perez, J.A.A. & P.R. Pezzuto. 2006. A pesca de arrasto de talude do Sudeste e Sul do Brasil: tendências da frota nacional entre 2001 e 2003. B. Inst. Pesca, 32(2): 127-150.         [ Links ]

Perez, J.A.A., P.R. Pezzuto, L.F. Rodrigues, H. Valentini & C.M. Vooren. 2001. Relatório da reunião técnica de ordenamento da pesca de arrasto das regiões sudeste e sul do Brasil. Notas Téc. Facimar, 5: 1-34.         [ Links ]

Perez, J.A.A., P.R. Pezzuto, A.L.S. Soares & R. Wahrlich. 2009. Deepwater fisheries in Brazil: history, status and perspectives. Lat. Am. J. Aquat. Res., 37: 513-541.         [ Links ]

Pezzuto, P.R. & E.M. Benincá. 2015. Challenges in licensing the industrial double-rig trawl fisheries in Brazil. Lat. Am. J. Aquat. Res., 43(3): 495-513.         [ Links ]

Pezzuto, P.R., J.A.A. Perez & R. Wahrlich. 2006. O ordenamento das pescarias de caranguejos-de-profun-didade (Chaceon spp.) (Decapoda: Geryonidae) no sul do Brasil. B. Inst. Pesca, 32(2): 229-247.         [ Links ]

Pio, V.M., P.R. Pezzuto & R. Wahrlich. 2016. Only two fisheries? Characteristics of the industrial bottom gillnet fisheries in southern Brazil and their implications for management. Lat. Am. J. Aquat. Res., 44(5): 882-897.         [ Links ]

Pipitone, C. 2012. Spatial approach to fisheries management in the Mediterranean region. Transversal Workshop on Spatial Based Approach to Fishery Management, Rome, 6-8 February 2012, 7 pp.         [ Links ]

R Development Core Team. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. []. Reviewed: 03 March 2016.         [ Links ]

Rassweiler, A., C. Costello & D.A. Siegel. 2012. Marine protected areas and the value of spatially optimized fishery management. PNAS, 109(29): 11884-11889.         [ Links ]

Reiss, H., G. Hoarau, M. Dickey-Collas & W.J. Wolff. 2009. Genetic population structure of marine fish: mismatch between biological and fisheries management units. Fish Fish., 10: 361-395.         [ Links ]

Reiss, H., S. Degraer, G.C.A. Duineveld, I. Kröncke, J. Aldridge, J.A. Craeymeersch, J.D. Eggleton, H. Hillewaert, M.S.S. Lavaleye, A. Moll, T. Pohlmann, E. Rachor, M. Robertson, E.V. Berghe, G. van Hoey & H.L. Rees. 2010. Spatial patterns of infauna, epifauna and demersal fish communities in the North Sea. ICES J. Mar. Sci., 67: 278-293.         [ Links ]

Rodrigues, R., S. Santos, M. Haimovici, U. Saint-Paul, I. Sampaio & H. Schneider. 2014. Mitochondrial DNA reveals population structuring in Macrodon atricauda (Perciformes: Sciaenidae): a study covering the whole geographic distribution of the species in the southwestern Atlantic. Mitochondrial DNA, 25(2): 150-156.         [ Links ]

Rossi-Wongtschowski, C.L.D.B., J.L. Valentin, S. Jablonski, A.C.Z. Amaral, F.H.V. Hazin & M. El-Robrini. 2006. O ambiente marinho. Capítulo 1. MMA. Programa REVIZEE. Avaliação do potencial sustentável de recursos vivos na zona económica exclusiva. Relatório Executivo, pp. 21-77.         [ Links ]

Sherman, K. & G. Hempel. 2009. The UNEP Large Marine Ecosystem Report: a perspective on changing conditions in LMEs of the world's Regional Seas. UNEP Regional Seas Report and Studies No. 182. United Nations Environment Programme. Nairobi, Kenya, 40 pp.         [ Links ]

Sparre, P. & S.C. Venema. 1997. Introdução à avaliação de mananciais de peixes tropicais. Parte 1: Manual. FAO Doc. Téc. Pesca, 306/1, Rev. 2: 404 pp.         [ Links ]

ter Braak, C.J.F. & P. Smilauer. 2002. CANOCO Reference manual and CanoDraw for Windows user's guide: Software for canonical community ordination (version 4.5). Microcomputer Power, Ithaca, 500 pp.         [ Links ]

Valentini, H. & P.R. Pezzuto. 2006. Análise das principais pescarias comerciais da região Sudeste-Sul do Brasil com base na produção controlada do período de 19862004. Série documentos Revizee: Score Sul, Instituto Oceanográfico - USP, São Paulo, 56 pp.         [ Links ]

Vasconcellos, A.V., D. Lima, F. Bonhomme, M. Vianna & A.M. Solé-Cava. 2015. Genetic population structure of the commercially most important demersal fish in the Southwest Atlantic: the whitemouth croaker (Micropogonias furnieri). Fish. Res., 167: 333-337.         [ Links ]

Vaz-Dos-Santos, A.M., C.L.D.B. Rossi-Wongtschowski & J.L. Figueiredo. 2009. Merluccius hubbsi (Teleostei: Merlucciidae): stock identification based on reproductive biology in the South-Southeast Brazilian region. Braz. J. Oceanogr., 57(1): 17-31.         [ Links ]

Vazzoler, A.E.A de M. 1991. Síntese de conhecimento sobre a biologia da corvina Micropogonias furnieri (Desmarest, 1823), da costa do Brasil. Atlântica, 13(1): 55-74.         [ Links ]

Wilen, J.E. 2004. Spatial management of fisheries. Mar. Res. Econom., 19: 7-19.         [ Links ]


Received: 23 December 2015;
Accepted:19 July 2016


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