Bycatch is one of the main causes of mortality of marine megafauna over the world. In the coastal waters of southern Brazil, bycatch in gillnet fisheries affects threatened species that use this region as a breeding and feeding area. The identification of hotspot areas of bycatch is necessary to design and prioritize efficient spatial-temporal closures that protect the largest possible number of threatened species of marine megafauna. In this context, the use of a multispecies approach is an important step towards planning effective fisheries management measures. This study has two main objectives: 1) to identify hotspot areas of bycatch in gillnet fisheries for the most threatened marine megafauna species on the continental shelf of Rio Grande do Sul (RS); 2) compare single species and multispecies mapping methods for the identification of these areas. To meet these objectives, data collected by onboard observers during fishing trips in the coastal commercial RS-based gillnet fleet, between 2013 and 2015 and between 2018 and 2020, were analyzed. For the identification of the areas, hierarchical Bayesian spatio-temporal models were implemented, using monospecific and multispecific approaches and a weighting system for the conservation status of the species. Both approaches provide similar results, identifying three bycatch hotspots according to the analyzed time frame. Based on our findings, we propose the creation of specific no-fishing areas in these regions. The suggested spatio-temporal closures would benefit several endangered species, also contributing to the recovery of fish populations.
Research Article
Identifying priority areas using a multispecies approach for the conservation of marine megafauna vulnerable species
https://doi.org/10.21203/rs.3.rs-1999077/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 10 Jan, 2024
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Bycatch
Threatened species
Multispecies models
Marine protected areas
Incidental catch in fishing nets, or bycatch, is one of the main causes of mortality of marine animals worldwide (Hall et al. 2000, Bellido et al. 2011, Northridge et al. 2016). The problem is even more serious for long-lived animals such as marine mammals, birds, marine turtles and elasmobranchs – this group of animals is referred to as megafauna – as they generally exhibit slow growth, late maturation and low reproductive potential (e.g. Lewison et al. 2004, Bonanomi et al. 2017). Among fishing gear, gillnets are of a particular concern, as they are known to be associated with relatively high mortality of several groups of marine megafauna (Northridge et al. 2016).
Coastal gillnet fisheries in southern Brazil emerged in the 1970s and became extensive from the early 1980s onwards (Reis et al. 1994, Vasconcellos et al. 2014). With the general trend of decreasing yields, in the last 30 years, the demersal gillnet fishery has undergone several changes, such as an increase in the quantity and size of nets and expansion of the areas of operation (Secchi et al. 2004, Vasconcellos et al. 2014). As a consequence of the fishing effort increase, the incidental capture of Franciscana dolphin Pontoporia blainvillei and other threatened species of marine megafauna has increased markedly (Secchi et al. 2004, Vasconcellos et al. 2014, Prado et al. 2021).
The Franciscana is endemic to the coastal waters of Brazil, Uruguay and Argentina and due to incidental catches in gillnet fisheries is possibly the cetacean species most impacted in the Western South Atlantic Ocean (Secchi et al. 2003a, 2021), classified as Vulnerable – VU by the International Union for Conservation of Nature (IUCN) red list (Zerbini et al. 2017). Bycatch have been recorded throughout its distribution range (Secchi et al. 1997, 2021, Ott et al. 2002), however, the coast of Rio Grande do Sul (RS) is where the incidental mortality rate of this species is highest (Ott et al. 2002, Secchi et al. 2003a, 2021). Annual catches are estimated to be of the order of hundreds to thousands of individuals (Prado et al. 2013, 2021, Secchi et al. 2021).
In the continental shelf of southern Brazil, industrial fishing with bottom gillnetts and trawling are the greatest threat to elasmobranch populations (Vooren and Klippel 2005a,b). The Brazilian guitarfish Pseudobatos horkelii, Angel sharks Squatina spp., Narrownose smoothhound Mustelus schmitti and Hammerhead sharks Sphyrna spp. were targeted in these fisheries since 1970s. After the mid 1980s, however, stocks of these elasmobranchs suffered a severe decline and rapidly collapsed (Vooren and Klippel 2005c, Vooren et al. 2005b,d). Based on Catch per Unit Effort (CPUE) data, a reduction of more than 80% in the populations of P. horkelii, S. guggenheim and S. occulta was estimated on the continental shelf of RS, between the late 1980s and early 1990s (Vooren et al. 2005d, Vooren and Klippel 2005c). According to Vooren and Klippel (2005a), this pattern has been observed in intense elasmobranch fisheries: an initial phase of high catches over a period of 5 to 20 years, followed by a rapid decline to collapse due to low productivity of the stock.
Since the enactment of the Instrução Normativa nº5 de 21/05/2004 (Brasil 2004) by the Brazilian Ministry of Environment, fishing for these elasmobranchs is prohibited, as well as their commercialization (Vooren and Klippel 2005b). However, all these species, among other threatened sharks, skates and rays species, continue to be caught as accompanying fauna by the gillnet and trawl fisheries and Squatina spp. are also incidentally caught in the pelagic longlining (Vasconcellos et al. 2014, Oddone et al. 2019, Awruch et al. 2019, Rigby et al. 2019a,b, Pollom et al. 2020a,b,c,d,e).
As an attempt to regulate the gillnet fisheries and decrease incidental catches of several threatened species, the Brazilian Ministry of Environment and Ministry of Fisheries and Aquiculture jointly published the Instrução Normativa Interministerial nº 12 de 22/08/2012 (INI 12/2012, Brasil 2012), which limits the maximum net lengths allowed, suspends the issuance of new licenses, creates procedures for remote satellite monitoring of vessels larger than 15m, and establishes no-fishing areas and spatial-temporal closures for gillnetting in southeastern and southern Brazil. Although these regulations represent an important attempt to diminish bycatch, they were not fully based on scientific knowledge. Prado et al. (2021) showed that even with full compliance, the current fishing regulations cannot reduce Franciscana bycatch to sustainable levels. Therefore, there is a need for an evaluation and identification of hotspot areas of bycatch – where the probability of incidental capture is highest – for the conservation of other species of marine megafauna, preferably using multispecies approaches that allow valuing diversity.
Indeed, according to Soykan et al. (2008), multispecies approaches are both logistically and analytically more difficult, but offer a more comprehensive understanding of the bycatch problem. Baum et al. (2003) stated that for marine reserves to be effective, conservation actions must consider impacts on the entire community of species, especially those most vulnerable, because emphasis on conservation of a single species, without control of effort, simply shifts the pressure from one threatened species to another and can have irreversible effects on biodiversity.
In this context, this study has a double objective: 1) to identify hotspots of bycatch of threatened species of marine megafauna, based in a multispecies approach, in the gillnet fishery in southern Brazil to define priority areas for their conservation; 2) compare single-species and multispecies mapping methods for the identification of these areas.
Study area
For guiding conservation strategies, four management areas for P. blainvillei – Fransciscana Management Areas (FMAs) – were proposed by Secchi et al. (2003b) and later refined by Cunha et al. (2014). The study area of the presente work, from Mostardas lighthouse (31°15'S) – an important reference point on the coast of RS – to Chuí (33°45'S) – located in the border with Uruguay, is part of FMA III and corresponds to the main fishing ground for both the gillnetting fleet based in Rio Grande (RS) and a large number of boats from Santa Catarina state (Fig. 1). The region is influenced by Subantarctic Shelf Water transported northward by the Malvinas/Falkland Current and Tropical Water and South Atlantic Central Water (SACW) transported southward by the Brazil Current (Palma et al. 2008). In addition, the region is influenced by nutrient-rich freshwater discharge of the Río de la Plata estuary and the Patos Lagoon mainly during winter, and by coastal upwelling of SACW due to the dominance of northeasterly winds during summer (Möller et al. 2008).These processes provide important nutrient input, and consenquently, high primary biological production which supports high abundance and biodiversity, including commercially important fish, making this area one of the most productivity fishing ground of Brazil (Castello et al. 1998, Odebrecht and Castello 2001, Vooren et al. 2005c).
Bycatch data and standardization
The fishing data used in this research were collected by onboard observers on vessels from the commercial coastal gillnet fleet based in Rio Grande, between June 2013 and March 2015 and between June 2018 and March 2020. For each set, observers recorded data on length, height and mesh size of the net, fishing time, location (latitude/longitude), depth, target species and incidentally caught species.
The gillnet fishery off RS coast operated in two well-defined seasons: the Whitemouth croaker Micropogonias furnieri fishery takes place in spring and summer, between October and April; and the Striped weakfish Cynoscion guatucupa and Argentine croaker Umbrina canosai season, occurs in the cold months, from May to September (Secchi et al. 1997, Boffo and Reis 2003, Vasconcellos et al. 2014). However, in the fishing trips analysed in this study it was observed that 42% of the settings targeting C. guatucupa and U. canosai occurred in spring and summer (October to March), and 22% of the settings directed to M. furnieri in autumn and winter (April to September).
For this reason, the data were separated according to the time of the year, being then divided into warm months (October to March) and cold months (April to September). As a consequence, for some species the data were insufficient to define the hotspot areas of incidental capture, when mapped separately (see Identifying hotspot areas by single species bycatch).
Only marine megafauna species that occurred in at least 10% of the total analysed hauls and are considered globally threatened by the IUCN (Zerbini et al. 2017, Awruch et al. 2019, Oddone et al. 2019, Rigby et al. 2019a,b, Pollom et al. 2020a,b,c,d,e) were selected (Table 1). Based on these criteria, 10 species were selected: the Franciscana (Pontoporia blainvillei), the Spotback skate (Atlantoraja castelnaui), the Eyespot skate (A. cyclophora), the Angular angelshark (Squatina guggenheim), the Hidden angelshark (S. occulta), the Narrownose smoothhound (M. schmitti), the Brazilian guitarfish (Pseudobatos horkelii), the Bignose fanskate (Sympterygia acuta), the Scalloped hammerhead (Sphyrna lewini) and the Smooth hammerhead (S. zygaena). The species were identified by the observers according to photographic guides prepared by researchers in this area.
In addition, only hauls take placed within the 100-m isobath were considered, as the species analysed are predominantly distributed over the inner continental shelf, at least at some stage of their life cycle (Danilewicz et al. 2009, Haimovici 1997, Vooren 1998, Menni and Stehmann 2000, Vooren and Klippel 2005c, Vooren et al. 2005b, Vooren and Oddone 2019).
In the data collected between 2013 and 2015, the number of individuals captured was not recorded, only the total weight per species in each set. To estimate the number of individuals, the average weight of each species per season of the year was calculated based ondata collected from 2018 to 2020. The total weight of each species was then divided by its average weight in the catches at each haul. In the first data set, the catches of the genera Squatina spp., Sphyrna spp. and Mustelus spp. were also not separated by species. The percentage of each of the species belonging to these genera from 2018 to 2020 was then calculated by season, and based on these results, the number of individuals of these species was estimated for the data from 2013 to 2015.
To define a ranking of the priority areas for conservation, among the hotspot zones identified, relative vulnerability values were assigned to the ten species according to their conservation status. The values of 0.3, 0.5 and 0.8 were considered, respectively, for Vulnerable (VU), Endangered (EN) and Critically Endangered (CR) species, according to their projected relative risk of extinction, following the IUCN Red Book’s criteria (IUCN 2019) (Table 1). Thus, for each fishing haul, the number of captured individuals of each species was multiplied by the value corresponding to their status, so that the species with a higher degree of threat received relatively higher vulnerability values.
Identifying hotspot areas of bycatch
A hierarchical Bayesian model was used to identify the hotspot areas of bycatch during warm months and cold months sepparatly. Inference and prediction were performed through the Integrated Nested Laplace Approximation (INLA) approach (Rue et al.2009) and corresponding R-INLA package (Lindgren and Rue 2015).
Two different approaches were used to identify hotspot areas of incidental captures: the first, a multispecies model – which considered all species together – whose response variable was defined as the sum of catches weighted by vulnerability indices divided by fishing effort (net area x fishing time), i.e. multispecies bycatch per unit effort (BmPUE). The second consisted in analysing each species separately, with the number of individuals caught divided by fishing effort (BPUE) as the response variable and overlaing all species by averaging the rasters. The species were also briefly discussed separately.
Bycatch per unit effort data are characterised by strong spatial dependence and a high proportion of zero. Due to these characteristics, we chose to use a Hierchical Bayesian hurdle spatial model (hereafter "hurdle model"), which is fairly suitable for such situations. The hurdle model allows the sequential input of the uncertainties associated with the entire sampling process (Pennino et al. 2018, Prado et al. 2021) and combines two separate frameworks: 1) presence/absence modelling, in order to obtain the probability of BmPUE occurrence (Y), and (2) modelling the BmPUE positive quantities (Z) conditioned to BmPUE presence. Thus, Yi and Zi were considered the spatial distributed occurrence and conditional-to-presence BmPUE, with i = 1, …, n refers to the spatial location.
The former (Yi) was modelled using a binomial distribution and the latter (Zi) a gamma distribution. The means of both variables were modelled through the logit and log link functions, respectively, to the spatial effects as:
where πi represents the probability of BmPUE occurrence at location i and µi and ϕ are the mean and dispersion of the conditional-to-presence BmPUE; α(Y) and α(Z) are the intercept and \({\xi }_{i}\) the spatial random effect at location i. The same model structure was applied to BPUE.
For all fixed-effect default Gaussian vague priors (i.e. mean 0 and variance of 100) implemented in R-INLA were used, i.e. they exert little influence on the posterior distribution. Thus, the results are determined by the data likelihood (Rufener 2016).
For the spatial component, the Stochastic Partial Differential Equations (SPDE) module was used (Lindgren et al. 2011). In this approach, the spatial effect depends on two hyperparameters: kappa and tau, which determine the amplitude and the total variance of the spatial effect, respectively. The SPDE INLA was used to create a triangulation around the sampled points and thus make the prediction for the remaining area of interest. Triangulation consists of dividing the area into triangles, creating a mesh, where observations are treated as initial vertices, and extra vertices are heuristically added to minimise the number of triangles required to cover the entire study area. These additional vertices are used as prediction sites (Pennino et al. 2013). Where there are more observations and therefore more information, the triangulation is denser, featuring smaller triangles.
The mesh, built using the inla.mesh.2d function, presents smaller triangles in the domain of interest and larger triangles around it, used to avoid edge effects. At the end of the process, a predictive posterior distribution of BmPUE was obtained for each point of the mesh, throughout the study area. Using these posterior distributions one can determine explicit probabilities on the estimation of BmPUE and BPUE (e.g. Pennino et al. 2013, Prado et al. 2021).
The layerStats function was used to compare the raster of the overlapping maps of the separate species with that of the multispecies model. This function makes a spatial correlation between the two layers through Pearson's correlation coefficient. The value of this coefficient can range from − 1 to 1; the closer to the extremes of the range, the stronger the correlation, with 1 indicating a perfect positive correlation (Figueiredo Filho and Silva Júnior 2009). This coefficient will also be used to check whether the species with the highest relative abundance had a large influence on the results of the multispecies model – if the coefficient is low, it means that these species probably had a lot of influence in determining the hotspots.
Fishing and bycatch data
In total, 51 fishing trips, on 18 fishing vessels (about 10% of the fleet), were monitored: 21 between June 2013 and March 2015, and 30 between June 2018 and March 2020. The number of fishing hours monitored per year ranged from 694.4 h to 1449.5 h. Based on data from 2013 to 2018, for which information on total fishing effort is available, this corresponds on average to approximately 1.5% of the total fleet effort. A total of 425 fishing hauls were analysed, 80% of which had incidental catches of at least one of the species studied (Fig. 1). During the warm months (October to March), 244 hauls were recorded, among which there were incidental catches in 198 (81%). Among the 181 hauls that occurred in the cold months (April to September), incidental catches occurred in 142 (78%). Bycatch data from the warm and cold months are described in Tables 2 and 3, respectively.
Identifying hotspots areas of multispecies bycatch
During warm months, the probability of bycatch is high throughout the coastal region south of Rio Grande, highlighting a hotspot area near the mouth of Patos Lagoon and another further south, near the Albardão lighthouse, where the spatial effect is even more significant. In this season, the catches are concentrated closer to the coast, mostly below 20 m depth (Fig. 2 a).
In the cold months, there are two well-defined bycatch hotspots. One located to the north of Rio Grande, and the other again in the Albardão region. During this season, the incidental catches occur further from the coast than in warm months, though most are within the 50 m isobath (Fig. 2 b).
Identifying hotspot areas by single species bycatch
In the warm months, the hotspots of bycatch for S. acuta, P. horkelii, M. schimitti and S. guggenheim were between the Rio Grande and Chuí, mainly up to the 20 m isobath (Fig. 3 e, g, i and k), while for A. castelnaui and S. occulta the areas were in the same region but extended beyond 50 m depth (Fig. 3 a and m). Unlike most species, hotspot areas of bycatch of A. cyclophora were further from the coast (Fig. 3 c) and those of S. lewini occurred throughout the inner continental shelf (below 50 m), except for the Albardão region, where the probability of incidental catch for this species is lower (Fig. 3 o). For S. zygaena and P. blainvillei, hotspots were identified in the Albardão region and to the north of Rio Grande (Fig. 3 q and r).
During cold months, areas to the north of Rio Grande and in the Albardão region, up to the 20 m isobath, are bycatch hotspots of A. castelnaui, S. occulta and P. blainvillei (Fig. 3 b, n and s). These areas extending to the 50 m isobath were identified as of bycatch hotspots of S. acuta, M. schmitti, S. guggenheim and S. lewini (Fig. 3 f, j, l and p). For A. cyclophora and P. horkelii, the hotspot areas of bycatch were identified to the north of Rio Grande, up to 50 m depth, and to the south, on the intermediate continental shelf, near the 100 m isobath (Fig. 3 d and h). S. zygaena occurred in only two hauls carried out during the cold months, hence it was not possible to determine bycatch hotspots for this specie at this time of the year.
Overlap between single and multispecies hotspot areas of bycatch
The Pearson’s correlation between the overlay raster of the monospecific maps and the multispecies model was high for both the cold (r2 = 0.79) and especially the warm months (r2= 0.89).
In the warm months, an area with high probability of bycatch was identified between latitude 32°30'S and Chuí, up to the 20 m isobath (Fig. 4 a). In the cold months, two bycatch hotspots were observed, one located north of Rio Grande and another extending from Albardão to near the border with Uruguay (Chui), both within the 50 m isobath (Fig. 4 b).
In this present study, we used both single and multispecies approach to produce predictive maps. The first allowed the identification of the hotspot areas of incidental capture of each species, showing which ones would benefit from the implementation of management measures in each of the multispecies areas. With this approach it was also possible to identify areas that are different from those observed for the species altogether, but that are also particularly important for some of these threatened species. On the other hand, the multispecies approach, by taking the vulnerability ranking into account, allows to identify priority areas for the conservation of the most threatened species, among the bycatch hotspots.
In this study, the vulnerability index considered only the conservation status of the species, due to the lack of biological data (e.g. abundance, population growth rate, fecundity) that would allow the productivity of each species to be determined more accurately. This method could favor the species that had higher relative abundance in the fishing hauls analyzed, but the high correlation between this method and the overlay of the monospecific maps shows that relative abundance did not have a significant impact on the final result.
Predictive habitat maps are extremely useful for the selection of priority areas and periods for implementing conservation measures (Pennino et al. 2013). Previous studies conducted along the coast of Rio Grande do Sul, such as that of Ivanoff et al. (2019) and Prado et al. (2021), used this approach to identify hotpots areas of bycatch for elasmobranchs and P. blainvillei, respectively. However, to date the mapping of bycatch hotspots of marine megafauna as a suite of species, demanding conservation actions, had not been performed in the subtropical western South Atlantic.
The identification of multispecies hotspot areas of bycatch is a key component to maximize the effectiveness of management strategies by avoiding or minimizing the chances of adopting solutions for a species and transfering the risk to another threatened species (e.g. Baum et al. 2003, Soykan et al. 2008, Lewison et al. 2009). Multispecies models, despite being complex and involving many parameters, provide a more ecosystem-based perspective and are likely to define priority areas with some level of protection to benefit diverse species.
The multispecies model identified three bycatch hotspots of threatened species of the marine megafauna in the coastal gillnet fishery on the continental shelf of RS, southern Brazil: 1) the Albardão region, 2) an area near the mouth of the Patos Lagoon (Rio Grande) and 3) another area north of Rio Grande. During the warm months (October to March), the highest probability of bycatch, for all species, occurs in the coastal region south of Rio Grande, with two large hotspot areas: one near the Patos Lagoon mouth (Rio Grande) and another further south, near the Albardão region, both known to be sites of high biological productivity (Abreu and Castello 1998, Muelbert et al. 2008). In the cold months (April to September), however, two hotspot areas for bycatch of marine megafauna stood out: one located north of the Rio Grande, coinciding with the main coastal gillnet fishing ground during this time of year (Monteiro et al. 2016), and the other again in the Albardão region, demonstrating that this is an important area for the conservation of marine megafauna throughout the year.
The single species analysis corroborates the results of the multispecies model. The Albardão region was shown to be a hotspot area of bycatch for P. blainvillei and all elasmobranch species, except S. acuta and S. lewini, in the warm months; and, for six species in cold months – S. guggenheim, S. occulta, A. castelnaui, A. cyclophora, S. acuta and P. blainvillei. The first five of these species are considered either Critically Endangered or Endangered and the last is Vulnerable to extinction. The probability of bycatch is also high for, at least, three species in the region located near the mouth of the Patos Lagoon, during the warm months – P. horkelii, S. guggenheim and S. occulta. During the cold season, the area north of Rio Grande is considered a priority for six of the species analysed, five of them being Critically Endangered (P. horkelii, M. schmitti, S. lewini, S. acuta and A. castelnaui) and one Endangered (A. cyclophora). For the latter, the hotspot areas in both periods are further from the coast, as this species inhabits deeper waters than most others, especially beyond 50 m depth (Oddone and Vooren 2004).
In the warm months, incidental catches of most species occur closer to the coast, in very shallow waters, mainly below 20 m depth, where the highest fishing effort concentrates, following the distribution pattern of the target species (Haimovici et al. 1996, Prado et al. 2021). During this period, small juveniles of several species of sciaenidae fish and adult M. furnieri are abundant near the shore (Haimovici et al. 1996), attracting both fishers and species that prey upon these fish, as is the case of P. blainvillei, S. guggenheim and A. castelnaui (Colonello 2005, Barbini and Lucifora 2012, Belleggia et al. 2019, Bassoi et al. 2020, Prado et al. 2021). Thus, extensive fishing effort in an area with a relatively high abundance of these animals results in high bycatch mortality (Secchi et al. 1997, Prado et al. 2021).
The fishing effort concentrated in shallow coastal waters south of Rio Grande also coincides with nursery areas of P. horkelii, S. guggenheim, S. occulta, S. lewini, S. zygaena and a regional population of M. schmitti (Vooren and Klippel 2005b, Vooren et al. 2005a). The hotspot areas of bycatch identified for all these five species, in the warm months, are located below 50 m depth. P. horkelii and Squatina spp. form aggregations in very shallow water, within the 20 m isobath (Vooren et al. 2005d, Vooren and Klippel 2005c, Vooren and Oddone 2019). In the case of P. horkelii, the females perform this migration for parturition. Thus, fisheries in these waters incidentally catch this specie at a critical point in their life cycle. Bycatch also affects the recruitment of these species, causing a major impact on the long-term viability of their populations (Vooren and Klippel 2005c, Vooren et al. 2005d). Most specimens of P. horkelii and S. guggenheim caught during the surveys conducted between 2018 and 2020 are below the size at first maturity (ECOMEGA, unpublished data), which highlights the severity of the problem and the need for conservation measures.
In the cold period, most catches took place further offshore than in the warm months. This is probably due to the wider distribution of the target species of the gillnet fishery at this time, especially C. guatucupa, hence the fishing effort decrease in shallow waters (Haimovici et al. 1996, Prado et al. 2021). At this time of year, the intense fishing effort seen north of the Rio Grande and in the Albardão region overlaps with areas where P. horkelii and Squatina spp. occur in high density, both on the mid-shelf as in the external one, towards where the adult individuals move after copulation, and in shallow waters close to shore, where recruits and juveniles of these species remain year-round and are caught as bycatch(Vooren and Klippel 2005a,b). Fishing activities at this time of the year also take place in areas considered critical for M. schmitti, S. occulta, S. lewini and S. zygaena, among other threatened species (Vooren and Klippel 2005b).
In general, the probability of incidental catch of marine megafauna is high when fishing effort overlaps with critical areas for the species, where a large part of their populations is concentrated in certain periods. Vooren and Klippel (2005a) state that to conserve elasmobranch species diversity, the characteristics of these fish (e.g. late maturation, low reproductive potential) determine objectives and actions of fishing control, aiming at the protection of their populations, especially in critical areas and times of the year, with the objective that neonates and adults survive in sufficient numbers for the maintenance and growth of these populations. According to Game et al. (2009), protected areas covering critical habitats have the potential to dramatically reduce the mortality of species, even if it protects only a portion of their distribution.
The intense fishing effort is the main threat to P. blainvillei (Secchi et al. 2003a, 2021) and elasmobranchs (Stevens et al. 2000, Vooren and Klippel 2005a, Bonanomi et al. 2017). Chelotti and Santos (2020) observed that, despite the prohibition of harvesting most sharks and rays that occur in southern Brazil, landings of some species are still recurrent, which may indicate that the mitigation measures adopted so far are not being effective. Prado et al. (2021) and Secchi et al. (2022) also showed that the regulations imposed by INI 12/2012 for the gillnet fishery were not sufficient to reduce the incidental catch of P. blainvillei to sustainable levels. This norm, among other restrictions, determines an exclusion zone within 5 nautical miles (nm) from Chuí to Albardão and 4 nm from Albardão to Torres (the northern limit of the State) form commercial gillneters. Therefore, more effective measures, such as the establishment of wider gillnet Fishing Exclusion Areas (FEAs) in the region and stronger enforcement, are urgently needed to achieve the conservation objectives of this and other species.
According to Ivanoff et al. (2019), the implementation of a Marine Protected Area (MPA) on the coast of RS would be a possible management tool to conserve the remaining fish stocks and the marine biodiversity. In addition to protecting important areas for the life cycle of threatened species by reducing fishing-related mortality (Vooren and Klippel 2005b, Heupel et al. 2018, Rolim et al. 2019), MPAs may contribute to the recovery of targeted fish stocks, benefiting both the species that prey upon these fish and the fishery itself (Gaylord et al. 2005, Gaines et al. 2009).
Several studies have demonstrated that, among various methods used to reduce incidental catches (e.g. modification of fishing gear, limitation of mesh size/length of nets), MPAs has proven to be effective tools for the conservation of megafauna threatened by incidental catches (e.g. Gormley et al. 2012, Rolim et al. 2019) being an effective measure in places where several species are incidentally caught and/or where different fisheries operate (Senko et al. 2013). Unlike most other measures employed to reduce bycatch (e.g. modification/installation of devices in nets), which usually focus on only one group, MPAs are likely to protect a wide diversity of species simultaneously. Therefore, they are considered more effective in areas where several fisheries operate in which the bycatch of several species occurs (Senko et al. 2013), as is the case of this present study. Furthermore, the surveillance of this type of measure can be done in a simple and low-cost way, through remote monitoring systems. In Brazil, for example, fishing vessels with a total length equal to or greater than 15 m, are monitored through the Programa Nacional de Rastreamento de Embarcações Pesqueiras por Satélite (PREPS).Baum et al. (2003) and Prado et al. (2021) emphasize that, in some situations, limiting fishing effort outside of fishing exclusion zones may be necessary so that the undesired effect of shifting effort and bycatch to other areas and vulnerable species, respectively, does not occur (e.g. Vaughan 2017). Murray et al. (2000) evaluated the effectiveness of a protected area in New England (USA) in reducing bycatch of Harbour porpoise (Phocoena phocoena) and observed that during the closure period, fishing effort shifted to adjacent areas without restrictions, and bycatch remained. Cole et al. (2021) observed that area closures in the Gulf of Saint Lawrence to prevent entanglement of Northern right whales (Eubalaena glacialis) in fishing nets changed the density and distribution of fishing effort so that the threat increased in areas outside the restricted zones. Nevertheless, Hastings et al. (2017) state that, in general, fisheries management using well-planned marine protected areas, combined with fisheries management outside MPAs, can provide broad economic and ecological benefits in multispecies contexts.
Thus, it is proposed the establishment of exclusion areas for gillnet fisheries, at least during part of the year, in places where the bycatch probability is highest. The candidate FEAs proposed here are i. the Albardão region (between 33°09'S and 33°56'S, up to the 20 m isobath during warm and 50 m in the cold months), that is highly impacted by gillnetting throughout the year; ii. the area near the mouth of Patos Lagoon mouth (between 32°06'S and 32°31'S, up to 20 m depth), where high bycatch occurs in the warm months; and iii. the area north of Rio Grande, which is a hotspot of bycatch for almost all species in the cold season (between 32°00’S and 32°30'S, up to about the 50 m isobath). The Albardão region is a particularly productive area of the continental shelf of RS, with a very relevant biogeographical, ecological and economic significance (Mattos and Ferreira 2018). Besides being fertilized by nutrients from the discharge of the La Prata river and the Patos-Mirim lagoon complex, the underground transport of fertile waters from the Mangueira Lagoon to the coastal waters contributes to further increase the productivity of this region (Muelbert et al. 2008, Attisano et al. 2008, Mattos and Ferreira 2018). The high biological production that results from the nutrient inputs brought by these waters supports a high biodiversity and important fishing activities (Muelbert et al. 2008). In addition, submerged features such as parcels and sandy banks with high ecosystem potential, which provide shelter, food and favour reproductive aggregations for bony fish and elasmobranchs characterise this area (Mattos et al. 2018). Benthic invertebrates that are important prey of several megafauna species analyzed in this present study are abundant in this area (Mattos and Ferreira 2018). Albardão is a hotspot area of bycatch for most of the marine megafauna species analysed in this study throughout the year, four of them being Critically Endangered and two Endangered. Therefore, the creation of a Marine Protected Area in this region is of utmost importance.
There are already several proposals for the creation of conservation units in this area. Mattos and Ferreira (2018) recommend the adoption of an integrated coastal management model, through the implementation of a broad Mosaic of Conservation Units, containing UCs of various categories. According to these authors, this proposal would safeguard the structure and ecological function of the ecosystem, promote the progressive recovery of threatened species and collapsed fishing stocks, the conservation of the integrity of the livelihoods of traditional artisanal fishing communities and the economic viability of industrial fisheries.
Prado et al. (2021) also identified this region as a priority area to become a no-fishing zone, aiming to protect P. blainvillei, recommend the implementation of temporary no-fishing areas. They propose that, in the Albardão region, fishing should be prohibited between April and September. These authors, also suggest that this area would bring further protection than the current no-fishing zones, as indicated in the INI 12//2012, helping to protect critical habitats for the species and thus favouring the recovery of the population.
The results presented here, however, demonstrate the relevance of the Albardão region for several species of the marine megafauna throughout the year. It is known that permanent fishing exclusion areas represent a type of management measure that is difficult to implement, given the potential social and economic negative impact to fishers (Pennino et al. 2018). However, to effectively achieve the conservation objective of recovering populations, the total exclusion of fisheries in the Albardão region, as pointed here, is critical given the relevance of this area for several threatened species considered in this study (and potentially others), whose populations are either collapsed or showing steep decline.
Vooren and Klippel (2005b) proposed the creation of five FEAs on the southern Brazilian continental shelf, aiming to exclude industrial fisheries operating with bottom trawls, gillnets and bottom-set longlines from the nursery grounds of several shark, skate and ray species. Even with the implementation of these FEAs, a large partion of the continental shelf would remain available for the fisheries, and the objective of protecting threatened elasmobranchs and part of their critical habitat would be met.
One of these exclusion areas covers both the region near Rio Grande and the area to the north, which are hotspots of bycatch in warm and cold months, respectively. The proposition of this FEA, provisionally named Conceição Corridor, would extend between 31°38'S and 32°12'S, until the 500 m isobath and would include areas where high densities of P. horkelii occur in winter and S. guggenheim and S. occulta throughout the year. It would also include a portion of the distribution area of the migratory population of M. schmitti and its coastal waters is nursery ground for regional populations of S. lewini and S. zygaena, among other threatened species. According to those authors, these aspects justify that the Conceição Corridor should be an exclusion area for all industrial fisheries using gillnets, bottom trawls and bottom-set longlines, and for artisanal and recreational fisheries that also incidentally catch the aforementioned species.
In fact, this present study demonstrates the need to reduce the fishing effort in these areas, especially up to 50 m depth, in order to protect the species described above, in addition to rays, franciscanas and sea turtles, which are also frequently incidentally caught within this area (Vooren et al. 2005a, Prado et al. 2013, 2021, Monteiro et al. 2016). However, when there is conficting interests, spatio-temporal closures of critical areas that overlap with fishing activities can be proposed as alternative to lessen impact to the industry (Pennino et al. 2018). In the case of these two areas, excluding fisheries in the period when the highest incidental catch rates are observed in each site would possibly be sufficient conserve important part of the marine megafauna diversity. This measure would benefit at least six Critically Endangered species (P. horkelii, S. occulta, M. schmitti, S. lewini, S. acuta and A. castelnaui) and two Endangered species (S. guggenheim and A. cyclophora). Nevertheless, and most importantly, the participation of the fishing sector is fundamental in defining FEA limits and the closure period(s), because the often-competing interests of fisheries and conservation need to be balanced before any regulatory decision is made. Álvarez-Fernández et al. (2017) evaluated several MPAs on the European coast of the Atlantic Ocean and observed that the areas with the greatest success in achieving their objectives are those that had stakeholders’ involvement in defining management plan (see also Agardy et al 2011).
The definition of priority areas for conservation through multispecies approaches is essential to achieve the goal of protecting marine megafauna species impacted by incidental catch in fisheries. These approaches help to determine the adequate size and location of areas that, when receiving management measures, can warrant the protection of as many species as possible.
When possible, it is recommended to do a complete risk analysis, estimating the productivity of each species and calculating the degree of overlap of fishing effort with their distribution area. The combination of productivity and susceptibility provides a more accurate estimate of species vulnerability. In this study, the lack of total fishing effort data for entire period sampled and of environmental data for modeling the species' habitat made this type of analysis impossible. However, this information is not essential for the identification of priority areas and this objective can be achieved even when these data are not available using, for example, the conservation status as was done here.
This study showed that there are three main hotspot areas of bycatch for ten threatened species of the marine megafauna in the coastal gillnet fishery in southern Brazil. These should be considered priorities for the implementation of fisheries management measures aimed at reducing fishing effort to protect biodiversity. The Albardão region stands out as an area of extreme importance. For the conservation of these species, the creation of a MPA or a FEA in this region is paramount.
Considering that several other species, in addition to those included in this study, such as sea turtles, are also impacted by other types of fishing gear, such as trawls in this same region (e.g. Monteiro et al. 2016), it is recommended that future studies using both multispecies and multifisheries approach are carried out. For such, it is also recommended to improve the determination of vulnerability index using species-specific biological attributes, such as population growth rate or related parameters including age at first reproduction, maximum observed age, fecundity, survival rates and abundance. This approach can improve the identification of critical areas and benefit an even greater number of species.
Acknowledgments
We thank the Projeto Toninhas Sul team – ECOMEGA/FURG and the researchers and students from Laboratório de Recursos Pesqueiros Demersais e Cefalópodes/FURG for field work, with special thanks to Suzana Paz, Luis Gustavo Cardoso and all onboard observers. We are greatful to all vessel’s capitains and crews that voluntarily colaborate with data collection. We are indebted to the Organization for the Conservation of South American Aquatic Mammals – YAQU PACHA and Nuremberg Zoo for continuously funding the franciscana research and the Fundação de Apoio à Universidade do Rio Grande – FAURG for the management of Projeto Toninhas Sul. This research is part of the MSc thesis written by MMS under the supervision of ERS and DSM and is a contribution of the Research Group Ecologia e Conservação da Megafauna Marinha – ECOMEGA/FURG/CNPq.
AUTHOR CONTRIBUTION STATEMENT
MMS, DSM and ERS conceived and designed the research. MMS, JHP and MGP performed the analysis. MMS wrote the manuscript under the guidance of ERS and DSM. ERS, DSM, JHP and MGP reviewed the manuscript.
Funding
This research was partially supported by an environmental offset measure established through a Consent Decree/Conduct Adjustment Agreement between Petrorio and the Brazilian Ministry for the Environment, with the Brazilian Biodiversity Fund – FUNBIO as an implementer. Financial support was also provided by Brazilian Ministry for the Environment (MMA) and the Organization for the Conservation of South American Aquatic Mammals – YAQU PACHA. National Council for Research and Development (CNPq) provided a MSc fellowship to MMS (130736/2019-8) and Research fellowships to ERS (PQ 310597/2018-8) and Coordination for the Improvement of Higher Education Personnel (CAPES) provided free access to many relevant journals through the portal “Periódicos CAPES”.
Competing interests
The authors have no competing interests to declare that are relevant to the content of this article.
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Table 1 Relative vulnerability values of the ten selected marine megafauna species defined according to conservation status
|
Species |
IUCN Status |
Relative value |
|
Pontoporia blainvillei |
Vulnerable |
0.3 |
|
Atlantoraja castelnaui |
Critically Endangered |
0.8 |
|
Atlantoraja cyclophora |
Endangered |
0.5 |
|
Squatina guggenheim |
Endangered |
0.5 |
|
Squatina occulta |
Critically Endangered |
0.8 |
|
Mustelus schmitti |
Critically Endangered |
0.8 |
|
Pseudobatos horkelii |
Critically Endangered |
0.8 |
|
Sphyrna lewini |
Critically Endangered |
0.8 |
|
Sphyrna zygaena |
Vulnerable |
0.3 |
|
Sympterygia acuta |
Critically Endangered |
0.8 |
Table 2 Incidental catch data for warm months (October to March). The average column refers to the average number of animals caught in each fishing haul during that time of the year; the maximum N represents the highest number of individuals caught in a single haul; total N is the total number of specimens of each species caught in the period analysed; the last column shows the percentage of hauls analysed which had incidental catches of the referred species
|
Species |
Mean (N/set) |
N max |
N total |
% of sets |
|
Pontoporia blainvillei |
0.09 |
2 |
21 |
7.8% |
|
Atlantoraja castelnaui |
0.74 |
13 |
181 |
17.2% |
|
Atlantoraja cyclophora |
1.42 |
40 |
347 |
16.8% |
|
Squatina guggenheim |
21.18 |
576 |
5169 |
61.5% |
|
Squatina occulta |
0.56 |
28 |
136 |
15.6% |
|
Mustelus schmitti |
8.00 |
340 |
1953 |
32.4% |
|
Pseudobatos horkelii |
4.47 |
161 |
1091 |
40.6% |
|
Sphyrna lewini |
1.98 |
82 |
483 |
25.4% |
|
Sphyrna zygaena |
1.69 |
102 |
412 |
20.1% |
|
Sympterygia acuta |
4.31 |
333 |
1051 |
34.4% |
|
TOTAL |
44.44 |
656 |
10844 |
81.1% |
Table 3 Incidental catch data for cold months (April to September). The average column refers to the average number of animals caught in each fishing haul during that time of the year; the maximum N represents the highest number of individuals caught in a single haul; total N is the total number of specimens of each species caught in the period analysed; the last column shows the percentage of hauls analysed which had incidental catches of the referred species
|
Species |
Mean (N/set) |
N max |
N total |
% of sets |
|
Pontoporia blainvillei |
0.16 |
4 |
29 |
12.2% |
|
Atlantoraja castelnaui |
0.81 |
14 |
146 |
24.3% |
|
Atlantoraja cyclophora |
0.46 |
12 |
83 |
15.5% |
|
Squatina guggenheim |
17.10 |
211 |
3095 |
57.5% |
|
Squatina occulta |
0.33 |
13 |
60 |
11.6% |
|
Mustelus schmitti |
10.75 |
333 |
1946 |
47.0% |
|
Pseudobatos horkelii |
3.83 |
186 |
694 |
47.0% |
|
Sphyrna lewini |
0.66 |
30 |
120 |
8.3% |
|
Sphyrna zygaena |
0.01 |
1 |
2 |
1.1% |
|
Sympterygia acuta |
1.98 |
55 |
358 |
33.7% |
|
TOTAL |
36.09 |
348 |
6533 |
78.5% |
No competing interests reported.