Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Trophic structure of a nektobenthic community exploited by a multispecific bottom trawling fishery in Northeastern Brazil

  • Alex Souza Lira ,

    Roles Conceptualization, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing

    alexliraufrpe@outlook.com

    Affiliations Departamento de Pesca e Aquicultura, Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil, IRD, Univ Brest, CNRS, Ifremer, LEMAR, F-29280 Plouzané, France

  • Flávia Lucena-Frédou,

    Roles Methodology, Writing – original draft, Writing – review & editing

    Affiliation Departamento de Pesca e Aquicultura, Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil

  • Frédéric Ménard,

    Roles Writing – review & editing

    Affiliation Aix Marseille Univ, Univ Toulon, CNRS, IRD, MIO, UM110, Marseille, France

  • Thierry Frédou,

    Roles Methodology, Writing – review & editing

    Affiliation Departamento de Pesca e Aquicultura, Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil

  • Júlio Guazzelli Gonzalez,

    Roles Formal analysis, Investigation, Methodology

    Affiliations Departamento de Pesca e Aquicultura, Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil, MARBEC, Univ. Montpellier, CNRS, IRD, Ifremer, 34095, Montpellier, France

  • Valdimere Ferreira,

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation Departamento de Pesca e Aquicultura, Universidade Federal Rural de Pernambuco (UFRPE), Recife, Pernambuco, Brazil

  • José Souto Rosa Filho,

    Roles Methodology, Writing – review & editing

    Affiliation Departamento de Oceanografia, Laboratório de Bentos (LABEN), Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil

  • Jean-Marie Munaron,

    Roles Data curation, Formal analysis, Investigation

    Affiliation IRD, Univ Brest, CNRS, Ifremer, LEMAR, F-29280 Plouzané, France

  • François Le Loc’h

    Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

    Affiliation IRD, Univ Brest, CNRS, Ifremer, LEMAR, F-29280 Plouzané, France

Abstract

We used complementary stable isotope (SIA) and stomach content (SCA) analyses to investigate feeding relationships among species of the nektobenthic communities and the potential ecological effects of the bottom trawling of a coastal ecosystem in northeastern Brazil. Carbon (δ13C) and nitrogen (δ15N) compositions were determined for five basal sources and 28 consumers, from zooplankton to shrimp and fish species. Fishes and basal sources showed a broad range of δ15N (fishes: 6.49–14.94‰; sources: 2.58–6.79‰) and δ13C values (fishes: -23.86 to -13.71‰; sources: -24.32 to -13.53‰), while shrimps and crabs exhibited similar nitrogen and carbon ratios. Six trophic consumer groups were determined among zooplankton, crustaceans and fishes by SIA, with trophic pathways associated mostly with benthic sources. SCA results indicated a preference for benthic invertebrates, mainly worms, crabs and shrimps, as prey for the fish fauna, highlighting their importance in the food web. In overall, differences between SCA and the SIA approaches were observed, except for groups composed mainly for shrimps and some species of high δ15N values, mostly piscivorous and zoobenthivores. Given the absence of regulation for bottom trawling activities in the area, the cumulative effects of trawling on population parameters, species composition, potentially decreasing the abundance of benthic preys (e.g., shrimps, worms and crabs) may lead to changes in the trophic structure potentially affect the food web and the sustainability of the fishery.

Introduction

Bottom trawling impacts marine habitats in three main aspects: i) physical, due to direct changes in the seabed structure [1], causing the resuspension of sediment (sediment’s matrix disruption) and injury or death of many benthic organisms [24]; ii) chemical, affecting the organic carbon mineralization [5,6] and re-inserting into the water column possible contaminants such as mercury [7]; and iii) biological, mainly given its high level of non-targeted catch [810], mostly composed of small sized individuals, usually juveniles [11,12].

In the food web, the fishing activity may act as regulator of the ecosystem, causing adverse ecological effects that could lead to major changes in the trophic interactions among species, consequently to marine habitat degradation [1316]. Particularly concerning the bottom trawling, direct food web effects are associated to the reduction of species richness and abundance [1719], however, important indirect consequences are usually disregarded [20]. The capture of non-targeted species by bottom trawling may be a potential risk for the ecosystem sustainability, not only by removing predators of high trophic level, but also prey of lower trophic levels, as the untargeted invertebrates [14,2123]. For example, a decline in prey availability for demersal fishes, could potentially reduce food intake and body condition [24], causing a trophic cascade effect, changing the ecosystem control equilibrium, either top-down or bottom-up, or even reaching the extreme collapse of the ecosystem [2527]. In this context, the effect of the predator-prey interactions into the ecosystem trophic structure may be accessed, either by the diet composition and natural markers (such as isotope analysis) [28], and also though ecosystem models (such as Ecopath) [29].

One of the traditional and most accessible ways to address the feeding habits of fish species is by qualitative and quantitative Stomach Content Analysis (SCA) [2830]. However, often when considering spatial and temporal variations, this approach may be misleading, providing only “snapshots” of the diet [31,32]. On the other hand, Stable Isotope Analysis (SIA) is one of the newest ecological tools in diet studies, providing information that are incorporated in the consumer tissues over a longer period of time [33], indicating resources poorly quantified by stomach contents methods due to regurgitation and digestion rates of preys [34,35]. Although less subject to temporal bias, the SIA approach are influenced, for example, by the type of tissue sampled, lipid concentration, climate season, life stage and size spectrum [3638].

However, even if SIA and SCA are inherently different techniques, both with considerable assumptions and caveats [39], the use of the these approaches as complementary tools, has been largely recommended [4043]. For example, increases of δ13C and δ15N may be related to the decrease in the biomass of benthic consumers, while the decrease of biomass of benthic preys causes the reduction in the trophic level of the species [45]. Currently, the assessment of the trawling impacts in the food-wed are restricted to SIA, when evaluating changes in carbon (δ13C) and nitrogen (δ15N) compositions and the trophic level of consumers or prey, and to SCA when considering the biomass of the preys [4446].

Although the Brazilian northeastern coast covers an extensive area and encompasses a wide range of environments, few studies of coastal trophic structure have been carried out, often focusing only on describing qualitatively and quantitatively the diet [4750], and in the functioning of the ecosystem [5153]. Even of great importance, the probable effect of the “disturbance” in the trophic web by fishing, especially those with high impact in the ecosystem (e.g., bottom trawling), has never been focused. Specifically, in Pernambuco, Northeast Brazil, despite the socio-economic relevance of the shrimp fishery, the activity is completely unregulated. Sirinhaém has the largest and most productive motorized fishing fleet among the coastal cities of Pernambuco, corresponding to 50% of the shrimp catch [54], being extremely important as income source for local population [55].

In this study, we investigated the trophic structure of the nektobenthic community exploited by the shrimp trawl fisheries in the State of Pernambuco, Northeastern Brazil, using stable isotopes (SIA) of carbon and nitrogen and stomach content (SCA) analyses. Our main aim is to determine the importance of the target species (shrimps) as prey for non-target species (bycatch fishes), also discussing the possible effects of the bottom trawling into the trophic interactions, which may affect the marine local community.

Material and methods

Study area and field sampling

In the west coast of the South Atlantic Ocean, mainly in Brazil, shrimps are exploited by a multispecies fishery along the entire coastline, mainly in shallow areas with motorized bottom trawl nets [56], being the Penaeidae the main target [57]. Three fishery systems, which differ in size, technology and volume of catch occur in the Brazilian waters: (i) the industrial fleet operating mainly in the North region (Amazon river estuarine system), Southeast and South Brazil; (ii) a semi-industrial fleet distributed from north to south of the country with similar technology of the artisanal fleet but with greater fishing power and catches; and (iii) artisanal fleet that operates along the entire coast, but specially in Northeast, characterized by higher number of people involved; low level of technology, capture and profit [58]. This later fishery system is present in our study area, Sirinhaém. This fishery has the proportion of fish bycatch: shrimp as 0.39:1 kg [59]. The fish bycatch is composed of 51 species, 38 genera and 17 families, primarily Pristigasteridae, Sciaenidae and Haemulidae, mostly zooplanktivore and zoobenthivore (e.g., Pellona harroweri, C. bleekerianus, Isophistus parvipinnis, Stellifer microps, Larimus breviceps, P. brasiliensis, C. nobilis and Haemulopsis corvinaeformis), which are often used as a byproduct (commercially valuable species) or consumed by the crew and local communities [59].

The coastal waters are influenced by nutrient supply from the Sirinhaém river, the climate is tropical, with a rainy season occurring between May and October. In terms of environmental condition, the rainfall ranges monthly from 20 to 450 mm·yr−1, the mean water surface temperature is 29°C, and the pH and salinity range between 8.0 and 8.7 and 23–37, respectively [60,61]. The shrimp fishery is artisanal and carried out near the coast [62] between 8 and 20 m depth, mainly inside or close to the Marine Protected Area of Guadalupe, around of Santo Aleixo Island, distant from 1.5 to 3 miles off the coast (Fig 1).

thumbnail
Fig 1. Study area located on the Pernambuco coast in northeast Brazil.

The Sirinhaém area, located on the Pernambuco coast in northeast Brazil. Depth was obtained from [63].

https://doi.org/10.1371/journal.pone.0246491.g001

Surveys to collect macroalgae, bycatch fishes and invertebrates (except zooplankton) were carried out quarterly with the approval by the Brazilian authorities, such as the Navy and the Ministry of the Environment (Sisbio—License n°34125), between 2014 and 2015 using the commercial bottom trawl fishing (length: 10 m; horizontal opening: 6.10 m; mesh size body: 30 mm; mesh size cod end: 25 mm). It was not required the approval by the Brazilian animal ethics committee, since species collected arrive dead onboard without any method of sacrifice and within the authorized fishery activity. In order to improve the data samples with other consumers of the bycatch not previously sampled, complementary data collections were carried out in October to December 2019 (see S1 Table for detail).

At each month, three trawls were performed during the daytime, between 10 and 20 m depth, for about 2 hours, with boat velocity varying between 1.6 and 3.7 knots. Zooplankton was sampled with a 300 μm mesh size plankton net hauled horizontally for 10 minutes at subsurface. In addition, basal food sources included suspended Particulate Organic Matter (POM) obtained by filtering 0.5–1.0 L of water through fiberglass filters (0.75 μm) and Sediment particulate Organic Matter (SOM) collected at low tide in a shallow area near the island from the top 2 mm layer of sediment using a tube core (2 cm of diameter) [37]. All compartments sampled and specimens caught were at once put on ice, then transported to the laboratory and stored in a freezer (-18°C) until the analysis. In laboratory, they were identified to species level and measured (standard length–SL for fishes and carapace length/diameter for shrimps and blue crabs).

Data analysis

Muscle samples (about 0.5g) from each fish, squid, blue crab and shrimp species were extracted, rinsed with distilled water to remove exogenous materials (e.g., remaining scales, bones and carapace). For POM, SOM and zooplankton (which comprehended only copepods), the whole organism/sample was used. Samples were dried in an oven at 60°C for 48h. Then, they were ground into a fine powder with a mortar and pestle.

POM, SOM and zooplankton samples were duplicated. The inorganic carbon was removed by acidification process prior to the δ13C analysis [64]. The sub-samples that were not acidified were analyzed for δ15N [31]. Samples were analyzed by continuous flow on a Thermo Scientific Flash EA 2000 elemental analyzer coupled to a Delta V Plus mass spectrometer at the Pôle Spectrométrie Océan (Plouzané, France). Results are expressed in standard δ notation based on international standards (Vienna Pee Dee Belemnite for δ13C and atmospheric nitrogen for δ15N) following the equation: (Eq 1)

Reference materials of known δ15N and δ13C were analyzed: USGS61, USGS62 and USGS63. The recommended values of the standards were reproduced within the confidence limits. For every six samples, a home standard (Thermo Acetanilide) of experimental precision (based on the standard deviation of the internal standard replicates) was used, indicating an analytical precision of ± 0.11‰ for δ13C and ± 0.07‰ for δ15N.

The carbon and nitrogen values of basal food sources and consumers of different trophic guilds [65] in Sirinhaém coast were investigated by the biplot of mean δ13C (±Standard deviation (SD)) and δ15N (±SD) values of each group/species. Due to the non-normality (Kolmogorov-Smirnov test) and non-homogeneity of variance (Bartlett test), the statistical significance of differences between individual δ13C and δ15N values of food sources, shrimp and fish bycatch species was assessed with the non-parametric Kruskal-Wallis test and pairwise multiple comparisons tested for subsequent comparisons in case of significant differences (p-value<0.05) [66].

From the mean values of δ13C and δ15N (objects) for each consumer species (descriptors), an Agglomerative Hierarchical Cluster (AHC) using the Ward’s minimum variance method based in Euclidian similarity resemblance matrix was performed in order to identify trophic groups of species [67,68]. To determine optimal number of clusters, the NbClust method proposed by Charrad et al. [69] was carried out. This method provides 30 indices to evaluate the relevant number of Clusters. In addition, the trophic groups obtained with AHC were compared using a Nonparametric multivariate permutational analysis of variance (PERMANOVA) [70]. All statistical analyses were performed considering a 5% significance level.

Stomach Content Analysis (SCA) were accessed for 52% of species (13 species, 52% of the total) caught in the same area, including fishes and shrimps from unpublished laboratory database, except Conodon nobilis [71]. For the remaining species (12), diet information was obtained from literature and detailed in the Tables 2 and S2. For local collected species, the stomachs were removed and weighed to the nearest 0.01 g and fixed in 10% formaldehyde within 48 h and then conserved in 70% alcohol. The contents of the individual stomachs were sorted, counted, weighed (g), and identified to the lowest possible taxonomic level.

To describe the diet composition of the consumers, the stomach content items were gathered in 9 prey groups (detritus, phytoplankton, zooplankton, worm, crab, mollusk, other crustaceans, shrimp and fish). The similarity of diet among species was accessed by AHC as explained earlier, using prey weight proportion (objects; %W) [55] for each consumer (descriptors).

To provide an overview comparison among SIA and SCA, the stomach contents data was graphically displayed through heatmaps (consumer x prey) along with a AHC, using prey weight proportion (%W) [72] for each consumer. In the heatmap approach, the individual values contained in a matrix were represented as color ramp within a range of %W value scale. In addition, the hierarchical cluster obtained from SIA was compared graphically to SCA and quantified by Baker’s Gamma Index (BGI) with permutation test [73,74] to identify the possible level of similarity among the dendrograms, and consequently the two approaches. BGI value ranges from -1 to 1, values close to 0 represents statistic difference between the two dendrograms (p<0.05), and values close to -1 and 1 reveals identical dendrogram.

All analyses were performed using the R environment [75], with packages vegan [76], cluster [77], NbClust [69] and dendextend [73] for the estimation the clusters, to identify the optimum cluster number and to measure the association between the two trees of hierarchical clustering respectively. Additionally, ggplot2 [78] and gplots [79] were used to generate graphics.

Results

Stable isotope compositions were analyzed in six invertebrate species and eighteen consumers—fish (167 samples), one zooplankton group (6 samples) and five basal sources (31 samples) (Table 1). Fishes and basal sources showed a broad range of δ15N (fishes: 6.49–14.94‰; sources: 2.58–6.79‰) and δ13C values (fishes: −23.86 to −13.71‰; sources: −24.32 to −13.53‰), while shrimps and Callinectes species exhibited similar values of nitrogen and carbon ratios (Table 1).

thumbnail
Table 1. Stable isotopes compositions of basal sources and consumers.

https://doi.org/10.1371/journal.pone.0246491.t001

Basal sources exhibited significant difference within the medians for both δ13C values (Kruskal-Wallis: χ2 = 17.814, p-value = 0.001) and δ15N (Kruskal-Wallis: χ2 = 23.668, p-value < 0.001) (Fig 2), for example between POM and SOM in δ15N, and the macroalgae Lobophora variegate and Gracilaria cervicornis in δ13C. The medians of δ13C values for the three shrimp species (Penaeus subtilis, P. schmitti and Xiphopenaeus kroyeri) were similar (Kruskal-Wallis: χ2 = 1.555, p-value = 0.459), as well as for δ15N values (Kruskal-Wallis: χ2 = 2.6428, p-value = 0.266). Significant differences were observed in δ15N and δ13C values (Kruskal-Wallis: χ2 = 63.44, p-value < 0.001; χ2 = 52.083, p-value < 0.001 respectively) for fish species, mostly due to Citharichthys spilopterus, Symphurus tesellatus, Eucinostomus argenteus and Diapterus auratus which showed the more depleted δ15N and δ13C values.

thumbnail
Fig 2. Biplot of carbon and nitrogen for basal sources and consumers.

Biplot of δ13C (‰) and δ15N (‰) values (mean ± SD) for basal sources (grey circles) and consumers (invertebrates and fishes) sampled off the Sirinhaém coast, northeastern Brazil. The dendrogram inserted in the right corner is from agglomerative hierarchical clustering (AHC) for 25 consumers representing the trophic groups, indicated by colours, where each node represents an individual species. Species abbreviations are: Sedimentary organic matter (SOM), Lobophora variegata (lob.var), Gracilaria cervicorni (gra.cer), Sargassum sp.(sar.sp), Particulate organic matter (POM), Zooplankton–(zoo), Penaeus subtilis (pen.sub), Penaeus schmitti (pen.sch), Callinectes danae (cal.dan), Callinectes ornatus (cal.orn), Xiphopenaeus kroyeri (xip.kro), Lolliguncula brevis (lol.bre), Citharichthys spilopterus (cit.spi), Diapterus auratus (dia.aur), Opisthonema oglinum (opi.ogl), Symphurus tessellatus (sym.tes), Diapterus rhombeus (dia.rho), Lutjanus synagris (lut.syn), Bairdiella ronchus (bai.ron), Chirocentrodon bleekerianus (chi.ble), Eucinostomus argenteus (euc.arg), Bagre bagre (bag.bag), Caranx hippos (car.hip), Micropogonias furnieri (mic.fur), Bagre marinus (bag.mar), Larimus breviceps (lar.bre), Stellifer microps (ste.mic), Isopisthus parvipinnis (iso.par), Conodon nobilis (con.nob) and Paralonchurus brasiliensis (par.bra).

https://doi.org/10.1371/journal.pone.0246491.g002

Among the basal sources, POM and SOM had maximum and minimum δ15N values respectively (6.79 and 2.85‰), while G. cervicornis and L. variegata showed the most depleted and enriched δ13C values, respectively (Fig 2). Between consumers, flatfish species (C. spilopterus and S. tesellatus) had the most depleted δ13C values and blue crab species (Callinectes danae and C. ornatus) were the most enriched. For the δ15N rates, zooplankton had the lowest, while Conodon nobilis, Paralonchurus brasiliensis and Lolliguncula brevis showed the highest values (Fig 2).

Cluster analysis performed on mean stable isotope ratio values for the consumer group significantly gathered species in 3 main groups (GR), divided on 2 to 3 sub-groups (Fig 2 inset) (PERMANOVA: F = 49.12; p-value < 0.001). Zooplankton, the only member of GR6, had the lowest δ15N.

Fish species associated to the seabed had relatively lower δ13C compared to the others and were separated into two groups, mojarras (D. rhombeus and D. auratus; GR5) and flatfish species (S. tesselatus and C. spilopterus; GR4) (Fig 2). The cluster GR3 regrouped the species of highest δ15N values, greater than 11‰, as piscivorous and zoobenthivore, while GR2 represented zooplanktivore, omnivore and zoobenthivore fishes of intermediate values of carbon (δ13C: -17.04 to -15.74‰) and nitrogen (δ15N: 9.58 to 10.98‰) (Fig 2 and Table 1). GR1 gathered the omnivorous or detritivores invertebrates, as shrimp and blue crab, with low δ15N values and enriched δ13C (Fig 2).

The diet description of the 25 consumers species/groups through SCA may be accessed in Table 2. Omnivorous and detritivores species, including shrimp (e.g., P. schmitti) and blue crabs (e.g., C. ornatus), showed high trophic plasticity, feeding from phytoplankton to fishes in proportions ranging, in average, from 8 to 25% for each group of prey (Table 2). Omnivorous fishes (e.g., E. argenteus and Micropogonias furnieri) were an exception, feeding predominantly on benthic fauna, as shrimp and worms, totalizing 60% and 95% of their diet, respectively (Table 2), while Opisthonema oglinum, classified as zooplanktivore, fed mainly on phytoplankton and zooplankton, which represented 83% of the diet (Table 2).

thumbnail
Table 2. Weight contribution (%) of each prey group in the diet of consumers off the Sirinhaém coast, northeastern Brazil.

https://doi.org/10.1371/journal.pone.0246491.t002

Shrimps, fishes, and worms were the main preys, contributing on average 50% of the stomach content of fishes and squids (L. brevis) (Table 2). In this group, P. brasiliensis was an exception, with a diet composed basically of detritus (58%) and shrimp (40%), similar to detritivorous species. Species classified as piscivores, Caranx hippos and Isopisthus parvipinnis, presented high percentage of fish in their diet, 82% and 57% respectively (Table 2).

Cluster analysis of SCA emphasized 6 significantly different main consumer groups (Fig 3) (PERMANOVA: F = 6.50; p-value < 0.001). Group 1 (six species) had diet based mainly on detritus, phytoplankton and zooplankton and worms, while the second group was composed of four species (e.g., flatfish and croaker) that fed mainly on worms (Fig 3 left). The group 3 (five species) and group 4 (four species) (e.g., Bagre marinus, Chirocentrodon bleekerianus and L. synagris), showed considerable variability in dietary items in the stomach contents dominated by crustaceans and fishes (Fig 3). In the last clusters of two (Group 5) and four species (Group 6), composed by piscivores or zoobenthivore species of high δ15N values (Fig 2 and Table 1), the main preys were fish or shrimps (Fig 3).

thumbnail
Fig 3. Heatmap of the diet proportion among consumers and prey.

The dendrograms inserted in the corners were made with agglomerative hierarchical clustering (AHC) based on diet proportion by stomach content data (left) and isotope composition data (right) off the Sirinhaém coast, northeastern Brazil. The grey boxes represent different groups based on stomach content data. Consumer abbreviations are given in Table 1 and colours based on clustering by isotope composition data. Acronyms for each prey are: Det–Detritus; Phy–Phytoplankton; Zoo–Zooplankton; Cra–Crab; Shr–Shrimp; Wor–Worm; Mol–Mollusc; Oth.cru—Other crustaceans and Fis–Fish.

https://doi.org/10.1371/journal.pone.0246491.g003

The species with high δ15N values (e.g., P. brasiliensis, C. nobilis and C. hippos), as well as shrimps (P. schmitti, P. subtilis, X. kroyeri) showed a similar grouping between the two approaches (SIA and SCA). However, in overall, differences in diagram clusters between stomach contents and the SIA approach were observed (Baker’s Gamma correlation coefficient = 0.20). Some species presented large grouping differences between the two approaches, mainly for species of the GR4 (e.g., C. spilopterus, and S. tesselatus) and zoobenthivores of the GR2 (e.g., O. oglinum, and E. argenteus), based in SIA clusters (Fig 3).

Discussion

The trophic ecology has long been assessed from diet composition to evaluate level of complexity, health and alterations of communities on aquatic ecosystems (e.g., rivers, estuaries, reefs and deep oceans) [47,9295]. Additional tools as the trophic natural markers provide information on the assimilated food, while the traditional approach of diet composition is based only on food intake. Comparing the two approaches improves the description and potentially minimizes errors in measuring the organism diets. Thus, by applying complementary methods—stable isotope and stomach content composition—we examined the trophic structure of a tropical ecosystem affected by shrimp bottom trawling, aiming to evaluate the importance of the shrimp species as food to coastal fauna and how the fishery exploitation of these resource may affect the ecosystem trophic functioning.

Firstly, some considerations should be made before the interpretation of our results. Although we have used most data from the study area and similar periods, we also utilized stomach content data from the literature, as proxy of the diet of some local species, which did not allow a direct comparison between methods (SCA and SIA), but rather a complementary approach. In addition, we decided not to apply the models to quantify the source importance in isotope approach (e.g., bayesian mixing model), given that our sampling did not take into account some of the known basal sources and benthic invertebrates, which could lead to potential misinterpretation of our results and conclusion as reported by [96]. Therefore, the results presented here are not intended to exhaustively describe the trophic dynamic of the study, but, despite their limitations, we were able to identify the predator and prey groups with major roles in the food-web, and how they could influence the ecosystem trophic dynamic in response to the shrimp fishery in Sirinhaém, northeast Brazil.

Differences on isotopic ratios occurred between SOM and POM. These variations among basal sources are expected [97] and reflects, for example, different contributions to organic deposition in coastal sediments [98100], which can be seasonally intensified with the increase of fluvial discharges during periods of heavy precipitation [101]. These differences allow the discrimination of two trophic pathways based on benthic or pelagic sources [102]. However, it usually can result in high range of isotopes ratios, given the high diversity of trophic guilds, [103,104]. In general, we found differences and similarities between SCA and the SIA approaches. For example, for shrimps and species of high δ15N values, mostly piscivorous and zoobenthivores, the two approached converged. However, we noticed some mismatches in our results for some zooplanktivore (e.g., O. oglinum), omnivore (e.g., C. ornatus and C. danae) and zoobenthivores species (e.g., B. marinus, L. synagris and Bairdiella ronchus). Generalist trophic habits associated with omnivores that feed on multiple trophic levels and taxonomic groups, introduce considerable uncertainty into diet patterns by SCA and SIA [105], mainly related to age-dependent trophic shifts [106]. Some studies report wide variations and even lack of correlation between SIA and SCA approaches [35,39,42], mainly related to aspects of differential size range [107], life stage [105], season [108], isotopic fractionation [109] and spatial-temporal scale [34].

For some zoobenthivores, isotopic niches often overlap with piscivorous [110], reflecting the opportunistic behavior of this group in an environment where food sources are highly available. Zoobenthivore fishes had wide feeding preferences [65,111], which would possibly provide large variations of δ15N composition [112,113]. However, the nitrogen ratios for this group slightly varied, indicating that they feed on food sources that have similar isotopic composition, consisting mostly of penaeid shrimps, small crabs and fishes in lower proportion. The availability and consequently the aggregation of prey can strongly influence the species feeding habitat patterns [114,115]; the predator would feed on prey largely available. Penaeidae shrimps are widely explored in the region, particularly the seabob shrimp (X. kroyeri), the most abundant one, and the pink (P. subtilis) and white shrimp (P. schmitti), with high market-values [62]. Although we have not evaluated the worms isotopic compositions, fish diet revealed a relative high contribution of this taxonomic group, mostly polychaets for some species (e.g., Eucinostomus argenteus–present study and Symphurus tesselatus—Guedes et al. [84]). Thus, polychaets should be considered as an additional important source of energy for the higher trophic levels.

Our findings with two complementary tools (SCA and SIA) helped to understand the contribution of benthic sources, the importance of crustaceans, especially shrimps, in transporting energy from food web base to upper trophic levels and bycatch species of high δ15N values, such as the top predators (e.g., I. parvipinis and C. nobilis), thus providing support to coastal food-web in Sirinhaém. The importance of the benthic community for the trophic functioning of the coastal zone, specifically crustaceans, has been reported in other ecosystems affected by bottom trawl fishing, for example, in southeast Brazil [116120], and in other parts of the world, such as Australia [121], Irish Sea [24] and North Sea [122]. The presence of large mud banks in these coastal areas, which usually favors large occurrences of benthic invertebrates, such as worms and crustaceans, explains this huge importance. In our study case, the fishing area in Sirinhaém is close to river mouth with depths ranging from 4 to 20 m, the seabed is composed of sand and predominantly mud zones, where most of the organisms and fishing effort is homogeneously concentrated. Hinz et al. [45] highlighted the negative effect of fishery trawling, removing not only fish and benthos, but also changing prey and predator relationships. The resuspension of sediment from trawling may cause death of a wide range of benthic organism [13], including benthic invertebrate preys of major role in energy transfer for the food-web, as for example in our case, the shrimps (e.g., X. kroyeri, P. subtilis and P. schmitti), crabs (e.g., C. ornatus and C. danae) and worms. The food-web dependence of the benthic invertebrates should also be considered in ecosystem approach to fisheries, since any regulation may therefore have consequences on both benthic prey and the consumers [45,123].

Specifically in Sirinhaém, since there are no fishing regulations [59], the cumulative effects of trawling on population parameters (e.g., size and food intake), species composition [124,125], potential decreasing the abundance of benthic preys and fish species may lead to intense changes in the trophic structure of the ecosystem, which may cause the trophic cascade effect (top-down or bottom-up) and potentially affect the food web and the sustainability of the fishery.

Supporting information

S1 Table. Complementary sampling information.

Mean, minima, maxima size, number of samples (n) in each quarter/year by species/group considered off the Sirinhaém coast, northeastern Brazil. For fish the size is related to standard length (cm); *for shrimps, carapace length (cm) and ** for mollusk, mantle length (cm).

https://doi.org/10.1371/journal.pone.0246491.s001

(DOCX)

S2 Table. Additional diet data information considered to present study off the Sirinhaém coast, northeastern Brazil.

Location and year of data, total length range used and whether seasonal or ontogenic characteristics were considered (yes (y) or no (n)).

https://doi.org/10.1371/journal.pone.0246491.s002

(DOCX)

Acknowledgments

We thank the BIOIMPACT Laboratory at Universidade Federal Rural de Pernambuco (UFRPE), because the present study could not have been done without the work of all its participants. In addition, the Laboratoire des sciences de l’environnement marin—LEMAR Laboratory at Université de Bretagne Occidentale (UBO) for the possibility of carrying out the analysis of stable isotopes.

References

  1. 1. Watling L, Norse EA. Disturbance of the seabed by mobile fishing gear: A comparison to forest clearcutting. Conserv Biol. 1998;12: 1180–1197.
  2. 2. Pham CK, Murillo FJ, Lirette C, Maldonado M, Colaço A, Ottaviani D, et al. Removal of deep-sea sponges by bottom trawling in the Flemish Cap area: conservation, ecology and economic assessment. Sci Rep. 2019;9: 1–13. pmid:30626917
  3. 3. Jones JB. Environmental impact of trawling on the seabed: A review. New Zeal J Mar Freshw Res. 1992;26: 59–67.
  4. 4. Kaiser MJ, Clarke KR, Hinz H, Austen MCV, Somerfield PJ, Karakassis I. Global analysis of response and recovery of benthic biota to fishing. Mar Ecol Prog Ser. 2006;311: 1–14.
  5. 5. De Borger E, Tiano J, Braeckman U, Rijnsdorp AD, Soetaert K. Impact of bottom trawling on sediment biogeochemistry: a modelling approach. Biogeosciences Discuss. 2020;2020: 1–32. Available: https://bg.copernicus.org/preprints/bg-2020-328/%0Ahttps://bg.copernicus.org/preprints/bg-2020-328/bg-2020-328.pdf.
  6. 6. Kaiser MJ, Collie JS, Hall SJ, Jennings S, Poiner IR. Modification of marine habitats by trawling activities: prognosis and solutions. Fish Fish. 2002;3: 114–136. https://doi.org/10.1046/j.1467-2979.2002.00079.x.
  7. 7. Sunderland EM, Amirbahman A, Burgess NM, Dalziel J, Harding G, Jones SH, et al. Mercury sources and fate in the Gulf of Maine. Environ Res. 2012;119: 27–41. pmid:22572623
  8. 8. Hiddink JG, Jennings S, Sciberras M, Szostek CL, Hughes KM, Ellis N, et al. Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance. Proc Natl Acad Sci. 2017;114: 8301–8306. pmid:28716926
  9. 9. Ortega I, André L, Felipe L, Dumont C. Response of soft-bottom macrobenthic assemblages to artisanal trawling fisheries in a subtropical estuary. Estuar Coast Shelf Sci. 2018;207: 142–153.
  10. 10. Zeller D, Cashion T, Palomares M, Pauly D. Global marine fisheries discards: A synthesis of reconstructed data. Fish Fish. 2017;19: 1–10.
  11. 11. Silva Júnior CA, Viana AP, Frédou FL, Frédou T. Aspects of the reproductive biology and characterization of Sciaenidae captured as bycatch in the prawn trawling in the northeastern Brazil. Acta Sci Biol Sci. 2015;37: 1.
  12. 12. Sedrez MC, Branco JO, Júnior FF, Monteiro HS, Barbieri E. Ictiofauna acompanhante na pesca artesanal do camarão sete-barbas (Xiphopenaeus kroyeri) no litoral sul do Brasil. Biota Neotrop. 2013;13: 165–175.
  13. 13. Hiddink JG, Jennings S, Kaiser MJ. Indicators of the ecological impact of bottom-trawl disturbance on seabed communities. Ecosystems. 2006;9: 1190–1199.
  14. 14. Jennings S, Pinnegar JK, Polunin NVC, Warr KJ. Impacts of trawling disturbance on the trophic structure of benthic invertebrate communities. Mar Ecol Prog Ser. 2001;213: 127–142.
  15. 15. Chassot E, Gascuel D, Colomb A. Impact of trophic interactions on production functions and on the ecosystem response to fishing: A simulation approach. Aquat Living Resour. 2005;18: 1–13.
  16. 16. Hinz H, Prieto V, Kaiser MJ. Trawl disturbance on benthic communities: Chronic effects and experimental predictions. Ecol Appl. 2009;19: 761–773. pmid:19425437
  17. 17. Ramalho SP, Lins L, Soetaert K, Lampadariou N, Cunha MR, Vanreusel A, et al. Ecosystem Functioning Under the Influence of Bottom-Trawling Disturbance: An Experimental Approach and Field Observations From a Continental Slope Area in the West Iberian Margin. Front Mar Sci. 2020;7: 1–15.
  18. 18. Preciado I, Arroyo NL, González-irusta JM, López-lópez L, Punzón A, Muñoz I, et al. Small-scale spatial variations of trawling impact on food web structure. Ecol Indic. 2019;98: 442–452.
  19. 19. Thrush SF, Dayton PK. Disturbance to marine benthic habitats by trawling and dredging: Implications for marine biodiversity. Annu Rev Ecol Syst. 2002;33: 449–473.
  20. 20. Hiddink JG, Kaiser MJ, Sciberras M, McConnaughey RA, Mazor T, Hilborn R, et al. Selection of indicators for assessing and managing the impacts of bottom trawling on seabed habitats. J Appl Ecol. 2020;57: 1199–1209.
  21. 21. Ortega I, Colling LA, Dumont LFC. Response of soft-bottom macrobenthic assemblages to artisanal trawling fisheries in a subtropical estuary. Estuar Coast Shelf Sci. 2018;207: 142–153.
  22. 22. Olsgard F, Schaanning MT, Widdicombe S, Kendall MA, Austen MC. Effects of bottom trawling on ecosystem functioning. J Exp Mar Bio Ecol. 2008;366: 123–133.
  23. 23. Collie JS, Hall SJ, Kaiser MJ, Poiner IR. A quantitative analysis of fishing impacts on shelf-sea benthos. J Anim Ecol. 2000;69: 785–798. pmid:29314001
  24. 24. Johnson AF, Gorelli G, Jenkins SR, Hiddink JG, Hinz H, Johnson AF, et al. Effects of bottom trawling on fish foraging and feeding. Proc Biol Sci. 2015;282: 20142336. pmid:25621336
  25. 25. Heath MR, Cook RM, Cameron AI, Morris DJ, Speirs DC. Cascading ecological effects of eliminating fishery discards. Nat Commun. 2014;5: 1–8. pmid:24820200
  26. 26. Pauly D, Christensen V, Dalsgaard J, Froese R, Torres F. Fishing down marine food webs. Science (80-). 1998;279: 860–863. pmid:9452385
  27. 27. Pinsky ML, Jensen OP, Ricard D, Palumbi SR. Unexpected patterns of fisheries collapse in the world’s oceans. Proc Natl Acad Sci U S A. 2011;108: 8317–8322. pmid:21536889
  28. 28. Braga RR, Bornatowski H, Vitule JRS. Feeding ecology of fishe: An overview of worldwide publications. Rev Fish Biol Fish. 2012;22: 915–929.
  29. 29. Silveira EL, Semmar N, Cartes JE, Tuset VM, Lombarte A, Ballester ELC, et al. Methods for Trophic Ecology Assessment in Fishes: A Critical Review of Stomach Analyses. Rev Fish Sci Aquac. 2020;28: 71–106.
  30. 30. Amundsen PA, Sánchez-Hernández J. Feeding studies take guts–critical review and recommendations of methods for stomach contents analysis in fish. J Fish Biol. 2019;95: 1364–1373. pmid:31589769
  31. 31. Pinnegar JK, Polunin NVC. Differential fractionation of δ13C and δ15N among fish tissues: Implications for the study of trophic interactions. Funct Ecol. 1999;13: 225–231.
  32. 32. Silveira EL, Semmar N, Cartes JE, Tuset VM, Lombarte A, Ballester ELC, et al. Methods for Trophic Ecology Assessment in Fishes: A Critical Review of Stomach Analyses. Rev Fish Sci Aquac. 2019;8249: 1–36.
  33. 33. Fry B. Stable Isotope Ecology. New York: Springer-Verlag; 2006.
  34. 34. Matley JK, Maes GE, Devloo-Delva F, Huerlimann R, Chua G, Tobin AJ, et al. Integrating complementary methods to improve diet analysis in fishery-targeted species. Ecol Evol. 2018;8: 9503–9515. pmid:30377518
  35. 35. Vinson MR, Budy P. Sources of variability and comparability between salmonid stomach contents and isotopic analyses: Study design lessons and recommendations. Can J Fish Aquat Sci. 2011;68: 137–151.
  36. 36. Boecklen WJ, Yarnes CT, Cook BA, James AC. On the Use of Stable Isotopes in Trophic Ecology. Annu Rev Ecol Evol Syst. 2011;42: 411–440.
  37. 37. Gonzalez JG, Ménard F, Le Loc’h F, Agrelli de Andrade H, Viana AP, Ferreira V, et al. Trophic resource partitioning of two snook fish species (Centropomidae) in tropical estuaries in Brazil as evidenced by stable isotope analysis. Estuar Coast Shelf Sci. 2019;226: 106287.
  38. 38. Le Loc’h F, Hily C. Stable carbon and nitrogen isotope analysis of Nephrops norvegicus/Merluccius merluccius fishing grounds in the Bay of Biscay (Northeast Atlantic). Can J Fish Aquat Sci. 2005;62: 123–132.
  39. 39. Petta JC, Shipley ON, Wintner SP, Cliff G, Dicken ML, Hussey NE. Are you really what you eat? Stomach content analysis and stable isotope ratios do not uniformly estimate dietary niche characteristics in three marine predators. Oecologia. 2020. pmid:32179976
  40. 40. Nielsen J, Christiansen JS, Grønkjær P, Bushnell P, Steffensen JF, Kiilerich HO, et al. Greenland shark (Somniosus microcephalus) stomach contents and stable isotope values reveal an ontogenetic dietary shift. Front Mar Sci. 2019;6: 125.
  41. 41. Varela JL, Sorell JM, Laiz-Carrión R, Baro I, Uriarte A, Macías D, et al. Stomach content and stable isotope analyses reveal resource partitioning between juvenile bluefin tuna and Atlantic bonito in Alboran (SW Mediterranean). Fish Res. 2019;215: 97–105.
  42. 42. Pacioglu O, Zubrod JP, Schulz R, Jones JI, Pârvulescu L. Two is better than one: combining gut content and stable isotope analyses to infer trophic interactions between native and invasive species. Hydrobiologia. 2019;839: 25–35.
  43. 43. Togashi H, Nakane Y, Amano Y, Kurita Y. Estimating the Diets of Fish Using Stomach Contents Analysis and a Bayesian Stable Isotope Mixing Models in Sendai Bay. In: Komatsu T, Ceccaldi H-J, Yoshida J, Prouzet P, Henocque Y, editors. Oceanography Challenges to Future Earth. Cham: Springer International Publishing; 2019. pp. 235–245.
  44. 44. Funes M, Marinao C, Galván DE. Does trawl fisheries affect the diet of fishes? A stable isotope analysis approach. Isotopes Environ Health Stud. 2019;55: 327–343. pmid:31179734
  45. 45. Hinz H, Moranta J, Balestrini S, Sciberras M, Pantin JR, Monnington J, et al. Stable isotopes reveal the effect of trawl fisheries on the diet of commercially exploited species. Sci Rep. 2017;7: 6334. pmid:28740093
  46. 46. Sinopoli M, Fanelli E, D’Anna G, Badalamenti F, Pipitone C. Efectos de un área de veda a la pesca de arrastre sobre la dieta y el nivel trófico de la merluza, Merluccius Merluccius, en el sur del Mar Tirreno. Sci Mar. 2012;76: 677–690.
  47. 47. Ferreira CEL, Floeter SR, Gasparini JL, Ferreira BP, Joyeux JC. Trophic structure patterns of Brazilian reef fishes: A latitudinal comparison. J Biogeogr. 2004;31: 1093–1106.
  48. 48. Dantas NCFM Silva Júnior CAB, Lippi DL Feitosa CV. Diel Variations and Ecological Aspects in Fish Assemblages of a Sandy Beach in the Semi-Arid Region of Northeast Brazil. Brazilian Arch Biol Technol. 2016;59: 1–11.
  49. 49. Vasconcelos Filho AL, Neumann-Leitão S, Eskinazi-Leça E, Schwamborn R, Oliveira AME, Paranaguá MN. Trophic interactions between fish and other compartment communities in a tropical estuary in Brazil as indicator of environmental quality. Adv Ecol Sci. 2003;18: 173–183.
  50. 50. Vasconcelos Filho AL, Neumann-Leitão S, Eskinazi-Leça E, Oliveira AME. Hábitos alimentares de peixes consumidores secundários do Canal de Santa Cruz, Pernambuco, Brasil. Trop Oceanogr Online. 2010;38: 121–128.
  51. 51. Lira AS, Angelini R, Le Loc’h F, Ménard F, Lacerda C, Frédou T, et al. Trophic flow structure of a neotropical estuary in northeastern Brazil and the comparison of ecosystem model indicators of estuaries. J Mar Syst. 2018;182: 31–45.
  52. 52. Freire KMF, Christensen V, Pauly D, Freire KMF, Christensen V, Pauly D. Description of the East Brazil Large Marine Ecosystem using a trophic model The columns of Table 2 in. Sci Mar. 2008;72: 477–491.
  53. 53. Lira AS, Lucena-Frédou F, Le Loc’h F. How the fishing effort control and environmental changes affect the sustainability of a tropical shrimp small scale fishery. Fish Res. 2021;235: 105824.
  54. 54. Tischer M, Santos MCF. Composição e diversidade da ictiofauna acompanhante de peneídeos no litoral sul de Pernambuco. Arq Ciência do Mar. 2003;36: 105–118.
  55. 55. Lira L, Mesquita B, Souza MMC, Leite CA, Leite , Ana Paula de Almeida Farias AM, Galvão C. Diagnóstico socioeconômico da pesca artesanal do litoral de Pernambuco. Instituto. Recife: Instituto Oceanário de Pernambuco; 2010.
  56. 56. Costa RC, Fransozo A, Freire FADM, Castilho AL Abundance and Ecological Distribution of the “Sete-Barbas” Shrimp Xiphopenaeus Kroyeri (Heller, 1862) (Decapoda: Penaeoidea) in Three Bays of the Ubatuba Region, Southeastern Brazil. Gulf Caribb Res. 2007;19: 33–41.
  57. 57. Lopes PFM. Extracted and farmed shrimp fisheries in Brazil: Economic, environmental and social consequences of exploitation. Environ Dev Sustain. 2008;10: 639–655.
  58. 58. Dias-Neto J. Proposta de plano nacional de gestão para o uso sustentável de camarões marinhos no Brasil. Dias-Neto J, editor. Ibama. Brasília: Ibama; 2011.
  59. 59. Silva Júnior CA, Lira AS, Eduardo LN, Viana AP, Lucena-Frédou F, Frédou T. Ichthyofauna bycatch of the artisanal fishery of Penaeid shrimps in Pernambuco, Northeastern Brazil. Bol do Inst Pesca. 2019;45: 1–10.
  60. 60. APAC. Agência Pernambucana de águas e clima. 2015 [cited 2 Feb 2017]. Available: http://www.apac.pe.gov.br/meteorologia/monitoramento-pluvio.php.
  61. 61. Mello MVL de. Parâmetros hidrológicos correlacionados com a biomassa e composição fitoplanctônica na região costeira adjacente a desembocadura do rio Sirinhaém (Pernambuco—Brasil). Universidade Federal dePernambuco. 2009.
  62. 62. Santos MDCF. Ordenamento Da Pesca De Camarões No Nordeste Do Brasil. Bol Técnico-Científico do CEPENE. 2010;18: 91–98.
  63. 63. Manso V, Correa I, Guerra N. Morfologia e sedimentologia da Plataforma Continental Interna entre as Prais Porto de Galinhas e Campos-Litoral Sul de Pernambuco, Brasil. Pesqui em Geociências. 2003;30: 17–25. Available: http://www.lume.ufrgs.br/handle/10183/22603.
  64. 64. Ryba SA, Burgess RM. Effects of sample preparation on the measurement of organic carbon, hydrogen, nitrogen, sulfur, and oxygen concentrations in marine sediments. Chemosphere. 2002;48: 139–147. pmid:12137051
  65. 65. Ferreira V, Le Loc’h F, Ménard F, Frédou T, Frédou FL. Composition of the fish fauna in a tropical estuary: the ecological guild approach. Sci Mar. 2019;83: 133.
  66. 66. Siegel S, Castellan NJ Jr. Nonparametric Statistics for The Behavioral Sciences. 2nd ed. New York: McGraw-Hill; 1988.
  67. 67. Davenport SR, Bax NJ. A trophic study of a marine ecosystem off southeastern Australia using stable isotopes of carbon and nitrogen. Can J Fish Aquat Sci. 2002;59: 514–530.
  68. 68. Madigan DJ, Carlisle AB, Dewar H, Snodgrass OE, Litvin SY, Micheli F, et al. Stable Isotope Analysis Challenges Wasp-Waist Food Web Assumptions in an Upwelling Pelagic Ecosystem. Sci Rep. 2012;2: 1–10. pmid:22977729
  69. 69. Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. J Stat Softw. 2014;61: 1–36. Available: https://www.jstatsoft.org/v061/i06.
  70. 70. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26: 32–46.
  71. 71. Lira AS, Viana AP, Eduardo LN, Fredóu FL, Frédou T. Population structure, size at first sexual maturity, and feeding ecology of Conodon nobilis (Actinopterygii: Perciformes: Haemulidae) from the coasts of Pernambuco, north-eastern Brazil. Acta Ichthyol Piscat. 2019;49: 389–398.
  72. 72. Hyslop EJ. Stomach contents analysis-a review of methods and their application. J Fish Biol. 1980;17: 411–429.
  73. 73. Galili T. dendextend: An R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics. 2015;31: 3718–3720. pmid:26209431
  74. 74. Baker FB. Stability of Two Hierarchical Grouping Techniques Case I: Sensitivity to Data Errors. J Am Stat Assoc. 1974;69: 440–445.
  75. 75. Core Team R. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available: https://www.r-project.org.
  76. 76. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. vegan: Community Ecology Package. R Packag version 24. 2017; https://CRAN.R-project.org/package=vegan. Available: https://github.com/vegandevs/vegan/issues%0Ahttps://github.com/vegandevs/vegan.
  77. 77. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K. cluster: Cluster Analysis Basics and Extensions. 2019.
  78. 78. Wickham H. ggplot2: elegant graphics for data analysis. Springer New York; 2009. Available: http://had.co.nz/ggplot2/book.
  79. 79. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Liaw WHA, Lumley T, et al. Package “gplots”. R package version 3.0.1. 2016. Available: https://cran.r-project.org/web/packages/gplots.
  80. 80. Schwamborn R. Influence of mangroves on community structure and nutrition of macrozooplankton in Northeast Brasil. Center for Tropical Marine Ecology, Bremen. 1997.
  81. 81. Branco JO, Verani JR. Dinâmica da alimentação natural de Callinectes danae Smith (Decapoda, Portunidae) na Lagoa da Conceição, Florianôpolis, Santa Catarina, Brasil. Rev Bras Zool. 1997;14: 1003–1018.
  82. 82. Olinto Branco J, Lunardon-Branco MJ, Verani JR, Schveitzer R, Souto FX, Guimarães Vale W. Natural diet of Callinectes ornatus Ordway, 1863 (Decapoda, Portunidae) in the Itapocoroy Inlet, Penha, SC, Brazil. Brazilian Arch Biol Technol. 2002;45: 35–40.
  83. 83. Coelho LI, Muto EY, Marian JEAR, Soares LSH. Contribution to the knowledge on the diet, feeding activity, and reproduction of Lolliguncula brevis (Blainville, 1823) in the coastal region off santos (São Paulo State). Bol do Inst Pesca. 2010;36: 225–236.
  84. 84. Guedes APP, Araújo FG, Azevedo MCC. Estratégia trófica dos linguados Citharichthys spilopterus Günther e Symphurus tessellatus (Quoy & Gaimard) (Actinopterygii, Pleuronectiformes) na Baía de Sepetiba, Rio de Janeiro, Brasil. Rev Bras Zool. 2004;21: 857–864.
  85. 85. Claudio Höfling J, Ishikawa Ferreira L, Borba Ribeiro Neto F, Aline Boer Lima P, Edwin Gibin T. Alimentação de peixes da familia Clupeidae do complexo estuarino-lagunar de Cananéia, SP, Brasil. Bioikos. 1998;12: 7–18.
  86. 86. Vasconcelos Filho AL, Neumann-Leitão S, Eskinazi-Leça E, Porto-Neto FF. Hábitos alimentares de consumidores primários da ictiofauna do sistema estuarino de Itamaracá (Pernambuco—Brasil). Rev Bras Eng Pesca. 2009;4: 21–31.
  87. 87. Costa SYL. Partição trófica de Lutjanus synagris (Linnaeus, 1758) e Lutjanus alexandrei (Moura & Lindeman, 2007) em sistema hipersalino tropical. Universidade Federal da Paraiba. 2013.
  88. 88. Muto EY, Malfara DT, Coelho LI, Soares LSH. Alimentação das sardinhas Pellona harroweri (Fowler, 1919) e Chirocentrodon bleekerianus (Poey, 1867), na região costeira de Santos, Estado de São Paulo. Oceanogr e mudanças globais São Paulo Inst Ocean. 2008; 287–302.
  89. 89. Pinheiro-Sousa DB, Silva NK, Pioski NM, Rocha ACG, Carvalho-Neta RNF, Almeida ZS. Aspectos alimentares e reprodutivos de Bagre bagre (Pisces, Ariidae) em um estuario da Ilha de Sao Luis, Maranhão, Brasil. Rev Bras Eng Pesca. 2015;8: 1–12.
  90. 90. Freret NV, Vanderli JA. Diet composition of Micropogonias Furnieri (Desmarest, 1823) (Teleostei, Scianidae) from Ribeira Bay, Angra Dos Reis, Rio De Janeiro. Bioikos. 2003;17: 33–37.
  91. 91. Bessa E, Santos FB, Pombo M, Denadai M, Fonseca M, Turra A. Population ecology, life history and diet of the shorthead drum Larimus breviceps in a tropical bight in southeastern Brazil. J Mar Biol Assoc United Kingdom. 2014;94: 615–622.
  92. 92. Hayden B, Palomares MLD, Smith BE, Poelen JH. Biological and environmental drivers of trophic ecology in marine fishes—a global perspective. Sci Rep. 2019;9: 1–10. pmid:30626917
  93. 93. Choy CA, Haddock SHD, Robison BH. Deep pelagic food web structure as revealed by in situ feeding observations. Proceedings Biol Sci. 2017;284. pmid:29212727
  94. 94. Noble RAA, Cowx IG, Goffaux D, Kestemont P. Assessing the health of European rivers using functional ecological guilds of fish communities: Standardising species classification and approaches to metric selection. Fish Manag Ecol. 2007;14: 381–392.
  95. 95. Barik SK, Bramha S, Behera D, Bastia TK, Cooper G, Rath P. Ecological health assessment of a coastal ecosystem: Case study of the largest brackish water lagoon of Asia. Mar Pollut Bull. 2019;138: 352–363. pmid:30660284
  96. 96. Lanari M, Possamai B, Copertino S, Garcia AM. Seasonal and El Niño Southern Oscillation-driven variations in isotopic and elemental patterns among estuarine primary producers: implications for ecological studies. Hydrobiologia. 2020.
  97. 97. Polunin NVC, Pinnegar JK. Trophic Ecology and the Structure of Marine Food Webs. Handbook of Fish Biology and Fisheries. John Wiley & Sons, Ltd; 2008. pp. 301–320. https://doi.org/10.1002/9780470693803.ch14
  98. 98. Gacia E, Duarte CM, Middelburg JJ. Carbon and nutrient deposition in a Mediterranean seagrass (Posidonia oceanica) meadow. Limnol Oceanogr. 2002;47: 23–32.
  99. 99. Vizzini S, Mazzola A. Sources and transfer of organic matter in food webs of a Mediterranean coastal environment: Evidence for spatial variability. Estuar Coast Shelf Sci. 2006;66: 459–467.
  100. 100. Gabara SS. Trophic structure and potential carbon and nitrogen flow of a rhodolith bed at Santa Catalina Island inferred from stable isotopes. Mar Biol. 2020;167: 1–14.
  101. 101. Garcia AM, Winemiller KO, Hoeinghaus DJ, Claudino MC, Bastos R, Correa F, et al. Hydrologic pulsing promotes spatial connectivity and food web subsidies in a subtropical coastal ecosystem. Mar Ecol Prog Ser. 2017;567: 17–28.
  102. 102. Claudino MC, Abreu PC, Garcia AM. Stable isotopes reveal temporal and between-habitat changes in trophic pathways in a southwestern Atlantic estuary. Mar Ecol Prog Ser. 2013;489: 29–42.
  103. 103. Vander Zanden MJ, Chandra S, Park S-KK, Vadeboncoeur Y, Goldman CR. Efficiencies of benthic and pelagic trophic pathways in a subalpine lake. Can J Fish Aquat Sci. 2006;63: 2608–2620.
  104. 104. Duffill Telsnig JI, Jennings S, Mill AC, Walker ND, Parnell AC, Polunin NVC. Estimating contributions of pelagic and benthic pathways to consumer production in coupled marine food webs. J Anim Ecol. 2019;88: 405–415. pmid:30548858
  105. 105. Davis AM, Blanchette ML, Pusey BJ, Jardine TD, Pearson RG. Gut content and stable isotope analyses provide complementary understanding of ontogenetic dietary shifts and trophic relationships among fishes in a tropical river. Freshw Biol. 2012;57: 2156–2172.
  106. 106. Layman CA, Winemiller KO, Arrington DA, Jepsen DB. Body size and trophic position in a diverse tropical food web. Ecology. 2005;86: 2530–2535.
  107. 107. Sweeting CJ, Barry J, Barnes C, Polunin NVC, Jennings S. Effects of body size and environment on diet-tissue δ15N fractionation in fishes. J Exp Mar Bio Ecol. 2007;340: 1–10. https://doi.org/10.1016/j.jembe.2006.07.023.
  108. 108. Brush JM, Fisk AT, Hussey NE, Johnson TB. Spatial and seasonal variability in the diet of round goby (neogobius melanostomus): Stable isotopes indicate that stomach contents overestimate the importance of dreissenids. Can J Fish Aquat Sci. 2012;69: 573–586.
  109. 109. Cresson P, Ruitton S, Fontaine M-F, Harmelin-Vivien M. Spatio-temporal variation of suspended and sedimentary organic matter quality in the Bay of Marseilles (NW Mediterranean) assessed by biochemical and isotopic analyses. Mar Pollut Bull. 2012;64: 1112–1121. pmid:22541382
  110. 110. Zhu Y, Newman SP, Reid WDK, Polunin NVC. Fish stable isotope community structure of a Bahamian coral reef. Mar Biol. 2019;166: 1–14.
  111. 111. Mourão KRM, Ferreira V, Lucena-Frédou F. Composition of functional ecological guilds of the fish fauna of the internal sector of the amazon estuary, Pará, Brazil. An Acad Bras Cienc. 2014;86: 1783–1800. pmid:25590716
  112. 112. Tue NT, Hamaoka H, Quy TD, Nhuan MT, Sogabe A, Nam NT, et al. Dual isotope study of food sources of a fish assemblage in the Red River mangrove ecosystem, Vietnam. Hydrobiologia. 2014;733: 71–83.
  113. 113. Medina Contreras D, Cantera Kintz J, Sánchez González A, Mancera E. Food Web Structure and Trophic Relations in a Riverine Mangrove System of the Tropical Eastern Pacific, Central Coast of Colombia. Estuaries and Coasts. 2018;41: 1511–1521.
  114. 114. Lawton RJ, Pratchett MS. Influence of dietary specialization and resource availability on geographical variation in abundance of butterflyfish. Ecol Evol. 2012;2: 1347–1361. pmid:22957144
  115. 115. Jacobson P, Gardmark A, Ostergren J, Casini M, Huss M. Size-dependent prey availability affects diet and performance of predatory fish at sea: A case study of Atlantic salmon. Ecosphere. 2018;9: e02081.
  116. 116. Santos MN, Rocha GRA, Freire KMF. Composición de la dieta de tres sciaenidos capturados en el nordeste de Brasil. Rev Biol Mar Oceanogr. 2016;51: 493–504.
  117. 117. Pombo M, Denadai MR, Turra A. Seasonality, Dietary Overlap and the Role of Taxonomic Resolution in the Study of the Diet of Three Congeneric Fishes from a Tropical Bay. PLoS One. 2013;8: 1–10. pmid:23405256
  118. 118. Rodrigues ES, Meira P de TF. Dieta alimentra de peixes presentes na pesca dirigida ao camarão sete-barbas (Xiphopenaeus kroyeri) na Baía De Santos e praia do Perequê, estado de São Paulo, Brasil. Boletim do Istituto de Pesca. 1988. pp. 135–146.
  119. 119. Sabinson L, Rodrigues-Filho J, Peret A, Branco J, Verani J. Feeding habits of the congeneric species Stellifer rastrifer and Stellifer brasiliensis (Acanthopterygii: Sciaenidae) co-occurring in the coast of the state of Santa Catarina, Brazil. Brazilian J Biol. 2015;75: 423–430. pmid:26132027
  120. 120. Willems T, De Backer A, Kerkhove T, Dakriet NN, De Troch M, Vincx M, et al. Trophic ecology of Atlantic seabob shrimp Xiphopenaeus kroyeri: Intertidal benthic microalgae support the subtidal food web off Suriname. Estuar Coast Shelf Sci. 2016;182: 146–157.
  121. 121. Reis M, Figueira WF. Diet and feeding habits of two endemic demersal bycatch elasmobranchs: Trygonorrhina fasciata & Dentiraja australis. Ichthyol Res. 2020;2006.
  122. 122. Hiddink JG, Rijnsdorp AD, Piet G. Can bottom trawling disturbance increase food production for a commercial fish species? Can J Fish Aquat Sci. 2008;65: 1393–1401.
  123. 123. van Denderen PD, van Kooten T, Rijnsdorp AD. When does fishing lead to more fish? Community consequences of bottom trawl fisheries in demersal food webs. Proc R Soc B Biol Sci. 2013;280. pmid:24004941
  124. 124. Shephard S, Brophy D, Reid DG. Can bottom trawling indirectly diminish carrying capacity in a marine ecosystem? Mar Biol. 2010;157: 2375–2381.
  125. 125. Hiddink JG, Johnson AF, Kingham R, Hinz H. Could our fisheries be more productive? Indirect negative effects of bottom trawl fisheries on fish condition. J Appl Ecol. 2011;48: 1441–1449.