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Fishes and Rivers of the State of Rio de Janeiro, Southeastern Brazil: Biogeographic and Diversity Patterns in a Historically Well-Sampled Territory

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10 July 2024

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Abstract
The fish fauna of Rio de Janeiro has been extensively studied, resulting in a comprehensive database of species collected over more than three centuries. This study aimed to identify fish species, their locations, and compile scattered information to aid in climate action and freshwater conservation prioritization and an evaluation of the sampling effort to date, as well as to identify patterns of diversity and distribution of freshwater ichthyofauna, delineate biogeographic units, and explore similarity relationships between areas. Analyzing data from nearly 25 ichthyological collections and literature on original species descriptions revealed 346 fish species: 172 freshwaters native, 22 allochthonous, and 152 marine species. The checklist includes updated species names. The sampling effort in Rio de Janeiro is high, especially in coastal lowlands. The findings indicate that inventory work is still needed in certain areas. Five bioregions of freshwater ichthyofauna were identified, along with six major areas of higher species richness. This biogeographic assessment underscores the diverse and distinctive freshwater fish fauna in the basins of Rio de Janeiro, with well-defined biogeographic units.
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Subject: Biology and Life Sciences  -   Ecology, Evolution, Behavior and Systematics

1. Introduction

The state of Rio de Janeiro possesses unique characteristics of the Atlantic Forest Biome, comprising a diverse array of ecological niches shaped by proximity to the coast, varied relief, soil types, and rainfall regimes. These factors have driven the evolution of a rich biotic complex, encompassing both forest and aquatic systems. In the mountainous areas, the Rio Paraíba do Sul stands out to the west in its middle courses, embedded in the Serra da Mantiqueira, and lower course towards the coast. To the east, smaller coastal rivers descend the slopes of the Serra do Mar. The coastal region is distinguished by sandbanks, dunes, mangroves, swampy forests, ponds, and swamps. In the central and southern portions of Rio de Janeiro, bays occupy the coastal lowlands, notably Guanabara Bay, Sepetiba Bay, and Ilha Grande Bay. The remaining rivers and streams regulate water flow, ensure soil fertility, control the climate, and protect escarpments and mountain slopes. In the northern region, lake systems dominate the landscape, with many lagoons such as Maricá, Saquarema, Araruama, and Feia.
Recent knowledge about rivers and fish in the southeastern Atlantic Forest territory is extensive [1,2], yet there remain fewer contributions that evaluate the complete set of species for specific areas within the Atlantic Forest [3,4,5]. A comprehensive understanding of the species composition across the heterogeneous regional landscapes is still lacking. Thus, updating and disseminating knowledge about this crucial biodiversity set is a priority.
Collections as repositories stimulate curiosity about species diversity, leading to the pursuit of answers to new questions. The fish fauna of Rio de Janeiro has been extensively studied [6,7,8,9,10,11,12,13,14], resulting in the creation of a robust database of species in museums and university collections over more than three centuries. However, there is a gap of comprehensive information. Investigating the fish species inhabiting each region, consolidating scattered information, and making it easily accessible is now more indispensable than ever, particularly for disseminating and valuing freshwater ecosystems in the face of rapid climate change. This study highlights the rivers of Rio de Janeiro, identifies the respective basins in a territorial context, and evaluates the distribution patterns of their fish species.

2. Materials and Methods

Hydrographic region 08 of Brazil corresponds to an area adapted from the Ottobasins division by Pfafstetter [15,16]. This territorial division, named Southeastern Atlantic, comprises river basins flowing into the Atlantic Ocean. It is bordered to the north by the hydrographic basin of the Rio São Mateus in Espírito Santo state and to the south by the hydrographic basin of the Rio Ribeira do Iguape in Paraná state. The hydrographic region is bordered to the west by the hydrographic regions of the São Francisco and Paraná [17]. Within hydrographic region 08, each river basin is recognized by a number, with number 1 being the northernmost river, the Rio São Mateus. Accordingly, each river basin in Rio de Janeiro is identified by a number between 20 and 38, in parentheses, as detailed below.

2.1. Study Area

The Rio de Janeiro state is separated into nine hydrographic areas – a political division towards governance of its territory [18], as follows: RH-1- Ilha Grande Bay drainage; RH-2- Rio Guandu and transposition with Paraíba do Sul (08.22e); RH-3- Middle stretch of Rio Paraiba do Sul basin (08.22d); RH-4- Rio Piabanha and tributaries at Paraíba do Sul basin (08.22c); RH-5- Guanabara bay drainage; RH-6- Lake region and Rio São João; RH-7- Rio Dois Rios and tributaries at Paraíba do Sul basin (08.22b); RH-8- Rio Macaé and Rio das Ostras; RH-9- Lower Rio Paraíba do Sul and Itabapoana (08.22a) (Figure 1).
The river basins along the Rio de Janeiro territory were divided into 24 groups (Figure 2). Among those areas five belongs to the Rio Paraíba Sul basin, as stated above (RH-9, RH-7, RH-4, RH-3 and RH-2). Additionally 19 are coastal basins and microbasins: Itabapoana Basin (08.20- Figure 2- A), São Francisco de Itabapoana Watersheds (08.21- Figure 2- B), Lagoa Feia Watersheds (08.23- Figure 2- H), Jurubatiba Watersheds (08.24- Figure 2-I), Macaé River Basin (08.25- Figure 2-J), Rio das Ostras Watersheds (08.26- Figure 2-K), São João River Basin (08.27- Figure 2-L), Una River Basin and Búzios Watersheds (08.28- Figure 2-M), Araruama Lagoon Watersheds (08.29- Figure 2-N), Saquarema Watersheds (08.30- Figure 2-O), Maricá Watersheds (08.31- Figure 2-P), Niterói Watersheds (08.32 Figure 2-Q), Guanabara Bay Watersheds (08.33 Figure 2-R), Rio de Janeiro Watersheds (08.34- Figure 2-S), Sepetiba Watersheds (08.35 Figure 2-T), Mangaratiba Watersheds (08.36 Figure 2-U), Angra dos Reis Watersheds (08.37- Figure 2-V), Paraty Watersheds (08.38- Figure 2-W) and Cairuçu Watersheds (08.39- Figure 2-X).

2.2. Species Data

Available records in fish collections from the Rio de Janeiro were consulted and had the identification confirmed. Each of the sampling points at the area had coordinates estimated from the locations indicated in the records. Initially, a curatorial survey of data was carried out in the fish collections of the Museu Nacional (MNRJ) and fish collection at the Museu de Biologia Mello Leitão (MBML). In the same way, records available in the database – SpeciesLink – Centro de Referência em Informação Ambiental [Reference Center for Environmental Information – CRIA] were consulted, and additionally the literature of species descriptions through which the data available in the collections of the AMNH, CAS (including CAS-SU), DZSJRP, FMNH, INPA, MCP, MCZ, MNRJ, MTD, MZFS, MZUEL, MZUSP, NPM, NRM, UF, UFRGS, UFRN, UFRJ, UFMT, UMMZ, UMZC, UNT, USNM and ZUEC. were inventoried (Supplementary file- Table S1). These institutional acronyms follow [19]. The records in collections were geo referred and plotted on a map of the Rio de Janeiro state. A total of 13,327 lots collected on the continent were inventoried (Supplementary file- Table S2).
The taxonomic classification of fishes follows [20]. Taxonomic problems are commented under section results.

2.3. Geographic Data

In georeferencing, for correspondence of the coordinates of each point, the values reported in the respective database were first used. When a small discrepancy was found between the reported value and the reported location, this value was adjusted according to the indication of the location. In cases when there was a large discrepancy between the indicated coordinates and the location, or when these coordinates were not available, the coordinates were estimated considering the available location information.
The hydrographic maps were adapted for use in the TrackMaker software [21], starting with version 1:25,000 made available by Instituto Brasileiro de Geografia e Estatística (IBGE) [22]. In the suggested location, information was used according to the names assigned to the rivers in this version, complemented by the names available in the IBGE topographic maps of 1:50,000. Municipality areas were calculated based on IBGE data [23].

2.4. Sampling Coverage Assessment

To evaluate the quality of sampling in the state, the index of total lots (il) per 100 km² and the index of sampling points (ip) per 100 km² were calculated. These indices were calculated for each municipality, hydrographic region, and group of basins, and these results were compared with the index for the entire state [24]. The sampling quality was considered average when found in the range of about 30%, poor when significantly below 30%, and good when above 30% (for both ilq and ipq). We evaluated the quality of sampling for the nine hydrographic regions, for the twenty-nine river basin divisions and additionally by municipality.

2.5. Biogeographic and Diversity Patterns

We applied the constancy index [25], used to determine which species are constant on a time scale: C = n/N * 100, where n = number of collection points where the species were captured and N = total number of collection points. Based on the results obtained, each species was classified as: constant, when C > 50%; accessory, when C varies between 25% and 50%; and accidental when C < 25%.
To assess differences in the fish fauna present in each hydrographic region of the study area the diversity indices absolute richness, Shannon diversity, equitability, and dominance were calculated, using species abundance data for each region [26]. Additionally, we traced diversity profiles, using the Rényi series, to compare the diversity among hydrographic regions. In the Rényi series, the minimum value of α results in an index that is equal to species richness (equability is not considered), and very high values of α assess only equability and disregard species richness [27]. In other words, we can only say that one river basin region is more diverse than another if the curves do not intersect. These analyses were performed using the PAST software [28].
Since determining the total number of species in an area is virtually impossible, especially in regions with high species richness, estimators are useful for extrapolating the observed richness and attempting to estimate the total richness through an incomplete sample of a biological community [29]. Consequently, we employed diversity estimators to assess the completeness of the species sampling for the hydrographic regions: the Chao 1 index, a simple estimator of the absolute number of species in a community. It is based on the number of rare species within a sample [30,31]; the iChao index estimator, which brought greater precision to the evaluation of the results [32]; and the AC estimator, Abundance-based Coverage Estimator [33]. This method works with the abundance of rare species (i.e. low abundance) [33,34]. However, unlike the previous estimator, this method allows the researcher to determine the limits for which a species is considered rare. In general, species with an abundance of between 1 and 10 individuals are considered rare. The estimated richness may vary as the abundance threshold is raised or lowered, and unfortunately there are no defined biological criteria for choosing the best range. Lastly, we adopted the Squares index, a richness estimator [35], designed to be more accurate than Chao-1 when abundance distributions are uniform.
We exclusively considered records of native freshwater species for the following spatial assessments. A bioregionalization analysis was performed using the Infomap Bioregions 2 algorithm [36], to subdivide the state of Rio de Janeiro into smaller biogeographical units. This algorithm uses species distribution data, even in cases of inconsistent sampling efforts. It employs an adaptive resolution method that generates a bipartite network of species and grids, followed by a clustering analysis to create bioregions based on the presence of specific taxa. The following parameters were employed: cell size ranging from 1/4° to 1° and cell capacity ranging from 5 to 1000 samples, 500 trials. The remaining settings followed the program defaults. We performed a cluster analysis using the binary matrix resulting from the bioregionalization algorithm to verify the similarity in faunal compositions between the bioregions generated by the UPGMA algorithm, Jaccard's similarity coefficient, in the PAST software [28].
A species richness interpolation analysis was performed to map diversity patterns using the spline interpolation method, which smooths out potential sampling gaps by creating a continuous surface of data values, in the BioDinamica model [37] of the Dinamica-EGO software [38]. The following parameters were used: raster grid size = 0.03, smoothing factor = 10, minimum of one sample per hexagon, using a delimitation mask of the Rio de Janeiro state.

3. Results

3.1. Rio de Janeiro According to the Collections - Diversity in Numbers

Lots sampled. The records of 16,299 lots from the collections were analyzed. Of these, 13,327 lots (81.77%) were sampled in continental waters of the Rio de Janeiro state. In addition, 2,890 (17.73%) were in the oceanic area and 82 (0.50%) could not be evaluated in its respective municipality. Of the lots sampled in inland waters, 985 (7.4%) are of marine origin, 498 (3.7%) are allochthonous freshwater, 11,724 (88.0%) are native to freshwater and 120 (0.9%) were not identified at the species level (Table 1).
From a geographical point of view, from the records in continental waters, it was possible to georeference 13,327 lots (81.74%), leaving 86 lots (0.53%) for which it was only possible to identify them at the state level.
Sampling index. The average sampling index based on lots of freshwater fish recorded for the state of Rio de Janeiro was 30.5 lots per 100 km². The average index of sampling points on the continent was 3.7 sampling points per 100 km².
Regarding the hydrographic regions (Table 2), three were considered poorly sampled: RH-3 (Middle Paraíba do Sul) with il = 12.1 and ip = 1.9; RH-7 (Rio Dois Rios) with il = 15.8 and ip = 1.6, and RH-9 (lower Paraíba do Sul and Itabapoana) with il = 12.8 and ip = 1.4. Four regions appear with a good number of lots and sampling points, and two are within the state average (Table 2, Figure 3).
Twenty-four municipalities (Table A1) appear with a good number of lots, 14 are within the state average and 54 appear with a poor number of lots. Regarding sampling points, there are 25 with a good number of points, 15 are within the state average and 52 have a poor number of sites (Figure 4). Eight municipalities (Armação dos Búzios, Arraial do Cabo, Belford Roxo, Macuco, Miracema, Porciúncula, São João de Meriti and Varre Sai) do not have records deposited in ichthyological collections for freshwater species, and the first two have only records of marine species.
In the evaluation of the basin groups (Table 3), in relation to the number of lots, 12 have a good number of lots, four are within the state average and eight have a poor number of lots. Regarding the sampling points, 11 have a good number of sites, five are within an average number and eight have a poor quality of sampling points.
Temporal variation. The growth of the records deposited in ichthyological collections after the year 2000 was 50% greater than all previous years (Figure 5). The earliest records are from 1832. From 1980 onwards, the collections had a significantly higher growth than in the entire previous period, continuously increasing since the year 2000.

3.1.1. Taxonomic Diversity

Entire set of records. Considering the entire set of records (Supplementary file- Table S3), 346 species were documented for the state of Rio de Janeiro (27 orders, 80 families and 207 genera), of which 172 are native freshwater species, 22 are allochthonous freshwater species and 152 are marine (Table 4). The orders Siluriformes (93 spp., 26.9% of the total) and Characiformes (50 spp., 14.5%) have the highest species richness, followed by Acanthuriformes and Cyprinodontidormes (33 spp. each, 9.5%). The most representative families of the total (Figure 5B) were Loricariidae (35 spp., 10.1%), Trichomycteridae (30 spp., 8.7%), Characidae (25 spp., 7.2%) and Rivulidae (21 spp., 6.1%).
Native freshwater records. Regarding native freshwater species (Figure 6, Table 5, Table A2) (eight orders, 26 families, 31 subfamilies and 82 genera), the orders Siluriformes and Characiformes were the most representative, with 86 (54.7% of the total freshwater species) and 40 (29.1%) species, respectively, followed by Cyprinodontiformes (32 species., 19.2%). The most representative freshwater families are Loricariidae (36 species, 20.9%), Trichomycteridae (30 species, 17.4%), Characidae (25 species, 14.5%) and Rivulidae (21 spp., 12.2%). The most representative native freshwater fish genera were Trichomycterus (21 species), Characidium, and Deuterodon (7 species).
Constancy index. A total of 1,680 sampling points with different coordinates were identified in the state of Rio de Janeiro. No species was classified as constant and only Geophagus brasiliensis appeared as an accessory species, having been recorded in 497 points (29.58%). The others were all accidental, such as Poecilia vivipara with 268 sites (16.95%) and Phalloceros harpagos with 267 (15.89%). It is noticeably that 72 species were collected in one sampling point only.
Considering only the native freshwater fishes, 1,553 sampling points with georrefered coordinates were identified. In this scenario of native species, again only Geophagus brasiliensis again appeared as an accessory species. The others were classified as accidental.
Non-parametric estimation and diversity indices. All species richness estimators indicate that the species catalogued for the hydrographic regions RH-1, RH-2 and RH-6 are within the expected range (Table 6). The greatest variation between the species catalogued for a region and the estimated species is in RH-9, which estimates the possibility of an increase of up to 28.0% (ACE) in the species richness indicated by the records, and in RH-4 with up to 25.7% (iChao-1).
The highest species richness was in the Guandu hydrographic region (RH-2), with 97 species, followed by the Baía de Guanabara hydrographic region (RH-5), with 89 species. The lowest richness was in the Baía da Ilha Grande hydrographic region (RH-1) with 40 species, the Médio Rio Paraíba do Sul hydrographic region (RH-3) and the Macaé e das Ostras hydrographic region (RH-8), both with 67 species. The Shannon index indicates to a greater diversity of RH-5 (H = 3.528), followed by the Rio Dois Rios hydrographic region (RH-7) (H = 3.516), and the Lagos São João hydrographic region (RH-6) (H = 3.366). The lowest diversity was identified at the Baixo Paraíba do Sul e Itabapoana hydrographic region (RH-9) (H = 2.513). The dominance index was higher in RH-9 (D = 0.1549), RH-8 (D = 0.1214), and the Piabanha hydrographic region (RH-4) (D = 0.1038). In contrast, the equitability index indicates that RH-9, RH-8 and RH-4 are the less even communities, and RH-7 the most even one.
The Guandu region (RH-2) was the one with the highest richness of native freshwater species with 97 species, followed by the Baía de Guanabara (RH-5) with 89 species, Baixo Paraíba do Sul e Itabapoana (RH-9) with 81 species, and Lagos São João (RH-6) with 80 species, a result consonant with the special interpolation of species richness. The Baía da Ilha Grande region (RH-1), on the other hand, presented the lowest richness, with only 40 species. The highest values of the Shannon diversity index were reached in hydrographic regions RH-2, RH-3, RH-5, RH-6, RH-7, areas also identified as hotspots of ichthyofaunistic biodiversity in Rio de Janeiro (see Figure 9[MI1] ).
The diversity profiles of the hydrographic regions using the Rényi series indicate that the diversity of the Baía da Ilha Grande region (RH-5) is lesser than the diversity of the Guandu region (RH-2) only when considering indices more dependent on species richness, but their diversities are very close when using the indices more dependent on equability (Figure 7a).
Despite the great difference between the richness of the hydrographic regions from the extremes south and north of Rio de Janeiro, Baía da Ilha Grande (RH-1, 40 spp.) and lower Paraíba do Sul and Itabapoana (RH-9, 81 spp.), the graph with the diversity curves (Figure 7b) indicates that it is not possible to define which region has the greatest diversity.
The diversity profile of the hydrographic regions that include only the tributaries of the Rio Paraíba do Sul (Middle Paraíba do Sul, RH-3; Piabanha, RH-4; Rio Dois Rios, RH-7) (Figure 7c) show a greater diversity in a lower part of Rio Paraíba do Sul (RH-7), while in the highest parts (RH-3, RH4) the crossing of the curves suggests that these diversities are not comparable and will alternate depending on the index that is considered.
The hydrographic region Baía de Guanabara (RH-5) is more diverse than the hydrographic region Lagos São João (RH-6), which in turn is more diverse than the hydrographic region Macaé and das Ostras (RH-8, Figure 7d).
Although Rio de Janeiro is a widely collected territory within the Atlantic Forest, this sampling is not equal. The quality indices of the sampled lots are good for an area varying between 20% and 32% of the state (according to the division per municipalities, basins or hydrographic regions), between 11% and 18% of the state area presented average sampling quality, and between 55% and 60% of the state presented poor sampling. These results suggest that there are still under-sampled areas in the territory that may be the target of future studies and inventories.

3.2. Spatial Patterns of Distribution

Five bioregions have been delineated based on patterns of distribution of the freshwater fish fauna (Figure 8A). These areas are herein referred to as Lower Rio Paraíba do Sul bioregion, Guanabara-Guandu bioregion, Middle Rio Paraíba do Sul bioregion, Costa Verde bioregion and Lagos bioregion.
  • Lower Rio Paraíba do Sul bioregion: The largest area in terms of territorial extension, this biogeographic unit is composed mainly of the lower Rio Paraíba do Sul basin and its tributaries, such as the Rio Muriaé, Rio Dois Rios and Rio Pomba, as well as independent basins such as the Rio Itabapoana (the geographical divide with the state of Espírito Santo), Rio Macaé and Rio São João, and coastal lagoon systems such as the Lagoa Feia. Species that delimit this bioregion include Listrura tetraradiata, Microglanis pleriqueater, and Trichomycterus puriventris, present in most of the drainages that compose this area. The bioregion has 114 species, of which 26 (22.8%) occur only in this area (e.g., Atlantirivulus janeiroensis, A. jurubatibensis, Bryconamericus tenuis, Characidium litorale, Delturus parahybae, Homodiaetus banguela, Ituglanis parahybae, Trichomycterus caipora, T. fuliginosus and T. vitalbrazili). Covering a large part of the basins of the state of Rio de Janeiro, many of the species present in these basins are common to several other drainages of the Atlantic Forest, such as fish species within the genera Deuterodon, Hypostomus and Trichomycterus.
  • Guanabara-Guandu bioregion: It consists mainly of the basins that drain the Baía de Guanabara and Baía de Sepetiba, such as the Rio Caceribu, Rio Guapimirim, Rio Macacu, Rio Roncador, Rio Suruí, Rio Guandu and the Jacarepaguá lagoon system. Species that delimit this bioregion include Kryptolebias caudomarginatus, Listrura nematopteryx, and Notholebias minimus. Presents 99 species, 17 of them (17.2%) occurring only in this area (e.g., Atlantirivulus guanabarensis, Australoheros macacuensis, Characidium grajahuense, Homodiaetus passarelli, Kryptolebias brasiliensis, Leptopanchax opalescens, L. sanguineus, L. splendens, Microglanis nigripinnis, Trichomycterus giganteus, and T. potschi).
  • Middle Rio Paraíba do Sul bioregion: Formed by tributaries of the middle course of the Rio Paraíba do Sul, such as the Rio Piraí, Rio Paquequer, Rio Preto, Rio Piabanha, and the Rio Paraíba do Sul. This bioregion is supported by the presence of species such as Parotocinclus bidentatus, Trichomycterus nigroauratus, and Trichomycterus florensis. The area presents 81 species, of which 11 (13.6%) occur only in this bioregion, especially species of the genus Trichomycterus, such as T. itatiayae, T. macrophthalmus, T. mariamole, and T. mirissumba.
  • Costa Verde bioregion: This biogeographic unit consists of small basins that flow into the Baía da Ilha Grande, as the drainages of the Rio Mambucaba, Rio Perequê-Açu, Rio Taquari and Rio Parati-Mirim. Supported by the presence of species such as Characidium japuhybense, Hemipsilichthys nimius, and Neoplecostomus paraty. The bioregion presents 40 species, of which nine (22.5%) occur only in this area (e.g., Atlantirivulus lazzarotoi, A. simplicis, Listrura costai, and Phalloceros enneaktinos).
  • Lagos bioregion: The smallest of the delimited areas, this bioregion consists of small drainages and lagoon systems that are part of the Região dos Lagos area, in the coast of Rio de Janeiro. It includes the drainages of the Rio Ubatiba, Rio Mato Grosso, coastal wetlands and lagoons of Maricá and Saquarema. The region is supported by the presence of species of rivulids such as Atlantirivulus maricensis, Leptopanchax citrinipinnis, Nematolebias papilliferus, and Notholebias fractifasciatus. It has 36 species, of which four (11.1%) only occur in that area.
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Figure 8. Bioregions of the freshwater icthyofauna within the territory of Rio de Janeiro (A) and Jaccard similarity among these areas (B).
Figure 8. Bioregions of the freshwater icthyofauna within the territory of Rio de Janeiro (A) and Jaccard similarity among these areas (B).
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The cluster analysis (cophenetic correlation: 0.93) indicates that the Lower Paraíba do Sul and the Guanabara-Guandu bioregions have approximately 47% of ichthyofaunistic similarity, and both present about 40% of congruence with the Middle Paraíba do Sul region (Figure 8B). The Costa Verde and Lagos bioregions present greater dissimilarity with hydrographic networks of the state, showing less than 20% of global similarity.
The interpolation of species richness shows six areas with the highest density of species in hydrographic regions of the state of Rio de Janeiro (Figure 9): (1) the middle Rio Paraíba do Sul, on the border with the state of São Paulo, in the vicinity of the Funil Reservoir, in the region of Itatiaia (RH-3); (2) the lower Rio Paraíba do Sul, in the region of the mouth of the Rio Dois Rios (RH-7); (3) the upper course of the Rio São João and Rio Macaé (RH-6 and RH-8); (4) several basins that flow into Baía de Guanabara (RH-5); (5) the Rio Guandu basin and adjacent basins (RH-2); and (6) Costa Verde basins (RH-1). In general, the region with the highest intensity of this index is specifically the sub-basin of the Rio Macacu and the Rio Guapimirim, followed by the upper Rio São João. In contrast, the regions with the lowest species richness were a stretch of the Middle Rio Paraíba do Sul, in the Rio das Flores basin; the upper course of the Rio Carangola and Rio Muriaé, in the Lower Rio Paraíba do Sul; and the region surrounding the lagoon system of Araruama and Cabo Frio.
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Figure 9. Interpolation of species richness within the state of Rio de Janeiro. The most relevant areas are highlighted with numbers 1-6.
Figure 9. Interpolation of species richness within the state of Rio de Janeiro. The most relevant areas are highlighted with numbers 1-6.
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4. Discussion

The perception that the rivers of the Atlantic Forest in Rio de Janeiro comprise sets of distinct areas of endemism for fish has been previously detailed by authors who have worked with the biome as a whole [39,40]. Subsequently, [41], in a global analysis of freshwater ecoregions, recognized three distinct ecoregions that include, in part, the territory of Rio de Janeiro: 329, Paraíba do Sul; 330, Ribeira de Iguape, and 352, Fluminense (see figure 1 in [41]). The division of Rio de Janeiro in bioregions of ichthyofaunal endemism recognized here roughly match the findings of [41]. The bioregions Lower Rio Paraíba do Sul and Middle Rio Paraíba do Sul partially adjusts to the ecoregion 329. The bioregion Costa Verde corresponds to part of the Ribeira do Iguape ecoregion. Additionally, the bioregions Guanabara-Guandu and Lagos almost corroborate the delineation of the Fluminense ecoregion [41].
The particularities of the relief and the configuration of the hydrographic basins in the Atlantic Forest biome in the territory of Rio de Janeiro include the Paraíba valley—depressed tectonic valley corridors along faults [42]—with rivers flowing between Serra do Mar and Serra da Mantiqueira Mountain ranges. Although the Rio Paraíba do Sul is the largest river system partially crossing the Rio de Janeiro territory, it was not recognized as the most diversified area for freshwater fishes. Possible reasons for this include the presence of numerous coastal drainages flowing from the Serra do Mar, as well as extensive areas of coastal lowlands with bays and lagoon systems. These diverse environments support a significant diversity of species. The mountainous nature with vertical escarpments and difficult access, that contributed to keep the mountain barrier impassable during the initial centuries of colonization [43], was also the feature that functioned as a geographical divide, isolating the Rio Paraíba do Sul from the coastal rivers. In the set of coastal lowlands, it is worth highlighting the special interest and diversity of freshwater species on the small river drainages at the Baía de Guanabara region. To mention its origin in a simplified way, Baía de Guanabara was geologically configured in the Cenozoic as a result of a tectonic depression, in a phenomenon known as block tipping, later filled by sediments derived from the erosion of adjacent mountain ranges, and in the Quaternary period drowned by the marine transgression that generated water mass of the bay [44]. Since the uplift of the mountains, the bay has been receiving sediments derived from the numerous rivers that drain into its interior. By its formation, the bay area holds a variety of living environments for freshwater forms, in different relief extracts. Additionally, the Sepetiba and Paraty bays were formed in a similar manner [42].
In the set of coastal lowlands, it is worth highlighting the special interest and diversity of freshwater species on the small river drainages at the Costa Verde bioregion. Despite corresponding to a small area of territory, the Costa Verde has a high endemism of species, with a unique set of stream fishes [14]. Being mostly fish of the first order, that is, intolerant of survival in brackish or salty waters, the process of isolation, dispersion and occupation of these species in the numerous small coastal streams has always been intriguing. The coastal drainages of the Costa Verde have particularities not observed in other areas, such as rivers flowing abruptly towards the coast, some of which are even devoid of coastal plain areas. Such topographic configuration of these small basins that flow from the mountains towards the sea has important implications for the aquatic biota, considering that isolated basins present similar fish fauna [45]. The coastal basins of the Atlantic Forest where the Serra do Mar is very close to the coast, such as those in the Costa Verde area, were influenced by climate change that caused oscillations in sea level during the Pleistocene. These transgressions and marine regressions generated isolation and connection between rivers [46]. The past connections among these coastal basins are hypothetized by [47] based on molecular evidence and paleodrainage reconstruction.
The orders Siluriformes and Characiformes were predominant in number of species, repeating a pattern commonly found in drainages of the Neotropical region and the Atlantic Forest biome [40,48], followed by Cyprinodontiformes. The predominance of these three orders is the result of characteristics that facilitate the occupation of species in different habitats and the great heterogeneity of environments available in the drainages of the state. This result is in line with several regional studies for Rio de Janeiro basins (e.g., [8,10,11,12,13,14,39,49].
Some species considered here have complex taxonomy, with phylogenies that need to be solved and probable species complexes, but we consider them as single taxa for analytic and inventory purposes. Rhamdia quelen has recently been redescribed and its distribution is now restricted to coastal basins from Rio de Janeiro south to the Rio Tubarão basin, in the state of Santa Catarina [50]. [51] describe seven subspecies of Gymnotus carapo for South American basins and suggest that the species appears to be absent from the coastal drainages of Northeastern and Southeastern Brazil, despite several records tentatively identified as G. carapo, G. aff. carapo or G. cf. carapo for these regions in museum collections and databases. Hoplias malabaricus and Synbranchus marmoratus most likely represent species complexes that require revision [52,53,54,55].
The greatest species richness in RH-2 (97 spp.) is associated with the fact that this hydrographic region encompassing coastal hydrographic systems is connected to the Rio Paraíba do Sul though links with the Rio Guandu system, which supplies water to the city of Rio de Janeiro. In contrast, the lowest species richness in RH-1 (40 spp.) is related to the geographic characteristics of this region, where the Serra do Mar is very close to the coast. The habitats are either characterized by steep rapids or estuarine plains.

5. Conclusions

This is the first time an evaluation of freshwater fishes in the Rio de Janeiro territory is accomplished with a geographical accuracy of data. We hope our results contribute for future research in the area and for conservation management measures of this complex and diverse territory. The distribution patterns of the fish species in the area corroborate previous studies of the Atlantic Forest and indicate that the recognized bioregions are in accordance with previous established biogeographic units.
The more sampled and diversified freshwater area —the vicinities of the Baía de Guanabara— may be due to the variety of environments in an area where rivers come from a slope mountainous area, the Serra dos Órgãos, with fast-flowing, clear water rivers and pebbles substrate, as well as swamps, slow flowing creeks and transitional environments such as mangroves in the coastal areas. Additionally, the proximity to the metropolitan area of Rio de Janeiro and its research centers historically facilitated the access. The less sampled regions in Rio de Janeiro corresponds to the middle reaches of Rio Paraíba do Sul at Rio das Flores, and also at its lower section, at traditional areas of coffee crops and nowadays mostly deforested, with river siltation and pollution.

Supplementary Materials

The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Table S1: Ichthyological collections with inventoried records from the state of Rio de Janeiro; Table S2: Records of fish species from the state of Rio de Janeiro listed for this study; Table S3: Inventory of all species recorded for the state of Rio de Janeiro.

Author Contributions

All authors designed the study; RFMP built the dataset and checked species distribution ranges. LMSS, FVG performed data curation and taxonomic validation. Biogeographical analysis was performed by FVG. Statistical analysis was performed by RFMP and FVG. All authors wrote the manuscript.

Funding

FGV was funded by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Finance Code 001, process no. 88887.512702/2020-00.

Data Availability Statement

Data is available in Supplementary Material Table S2.

Acknowledgments

We extend our gratitude to M. Gianeti, O. Oyakawa (MZUSP), M. R. Britto, (MNRJ), for providing collection records. We are grateful to M.R. Britto, P.A. Buckup (MNRJ) and J. P. da Silva (MBML) for the courtesy extended during the visit to the institutions. FVG is grateful to CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Finance Code 001, process no. 88887.512702/2020-00) for his Ph.D. scholarship. This work received laboratory support from the Instituto Nossos Riachos (INR).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Quality of sampling per municipality.
Table A1. Quality of sampling per municipality.
Municipality Area (km2) Lots Points Il Ilq Ip Ipq
Angra dos Reis 813.21 541 56 66.5 Good 6.9 Good
Aperibé 94.54 2 2 2.1 Poor 2.1 Poor
Araruama 638.15 34 4 5.3 Poor 0.6 Poor
Areal 110.72 1 1 0.9 Poor 0.9 Poor
Armação dos Búzios 70.98 0 0 0.0 Poor 0.0 Poor
Arraial do Cabo 152.11 0 0 0.0 Poor 0.0 Poor
Barra do Piraí 584.61 41 8 7.0 Poor 1.4 Poor
Barra Mansa 547.13 40 7 7.3 Poor 1.3 Poor
Belford Roxo 78.99 0 0 0.0 Poor 0.0 Poor
Bom Jardim 382.43 19 5 5.0 Poor 1.3 Poor
Bom Jesus do Itabapoana 596.66 32 8 5.4 Poor 1.3 Poor
Cabo Frio 413.58 65 14 15.7 Poor 3.4 Average
Cachoeiras de Macacu 954.75 927 83 97.1 Good 8.7 Good
Cambuci 558.28 6 2 1.1 Poor 0.4 Poor
Campos dos Goytacazes 4 032.49 608 75 15.1 Poor 1.9 Poor
Cantagalo 747.21 48 6 6.4 Poor 0.8 Poor
Carapebus 304.89 293 20 96.1 Good 6.6 Good
Cardoso Moreira 522.60 24 3 4.6 Poor 0.6 Poor
Carmo 305.75 252 23 82.4 Good 7.5 Good
Casimiro de Abreu 462.92 343 32 74.1 Good 6.9 Good
Comendador Levy Gasparian 108.64 1 1 0.9 Poor 0.9 Poor
Conceição de Macabu 338.26 114 15 33.7 Average 4.4 Average
Cordeiro 113.05 8 1 7.1 Poor 0.9 Poor
Duas Barras 379.62 3 1 0.8 Poor 0.3 Poor
Duque de Caxias 467.32 233 38 49.9 Good 8.1 Good
Engenheiro Paulo de Frontin 139.38 31 4 22.2 Average 2.9 Average
Guapimirim 358.44 317 28 88.4 Good 7.8 Good
Iguaba Grande 50.98 7 2 13.7 Poor 3.9 Average
Itaboraí 429.96 127 10 29.5 Average 2.3 Poor
Itaguaí 282.61 167 16 59.1 Good 5.7 Good
Italva 291.19 18 2 6.2 Poor 0.7 Poor
Itaocara 433.18 118 8 27.2 Average 1.8 Poor
Itaperuna 1 106.69 319 16 28.8 Average 1.4 Poor
Itatiaia 241.04 230 38 95.4 Good 15.8 Good
Japeri 81.70 59 3 72.2 Good 3.7 Average
Laje do Muriaé 253.53 7 1 2.8 Poor 0.4 Poor
Macaé 1 216.99 886 82 72.8 Good 6.7 Good
Macuco 78.36 0 0 0.0 Poor 0.0 Poor
Magé 390.78 308 44 78.8 Good 11.3 Good
Mangaratiba 367.82 67 12 18.2 Poor 3.3 Average
Maricá 361.57 174 35 48.1 Good 9.7 Good
Mendes 95.32 15 2 15.7 Poor 2.1 Poor
Mesquita 41.17 1 1 2.4 Poor 2.4 Poor
Miguel Pereira 287.93 98 16 34.0 Average 5.6 Good
Miracema 303.27 0 0 0.0 Poor 0.0 Poor
Natividade 387.07 7 1 1.8 Poor 0.3 Poor
Nilópolis 19.39 1 1 5.2 Poor 5.2 Good
Niterói 133.76 35 8 26.2 Average 6.0 Average
Nova Friburgo 935.43 182 32 19.5 Poor 3.4 Average
Nova Iguaçu 520.58 479 58 92.0 Good 11.1 Good
Paracambi 190.95 24 3 12.6 Poor 1.6 Poor
Paraíba do Sul 571.12 15 4 2.6 Poor 0.7 Poor
Paraty 924.30 609 84 65.9 Good 9.1 Good
Paty do Alferes 314.34 8 2 2.5 Poor 0.6 Poor
Petrópolis 791.14 143 25 18.1 Poor 3.2 Average
Pinheiral 82.25 2 1 2.4 Poor 1.2 Poor
Piraí 490.26 120 17 24.5 Average 3.5 Average
Porciúncula 291.85 0 0 0.0 Poor 0.0 Poor
Porto Real 50.89 41 4 80.6 Good 7.9 Good
Quatis 284.83 51 8 17.9 Poor 2.8 Poor
Queimados 75.93 33 2 43.5 Good 2.6 Average
Quissamã 719.64 267 38 37.1 Average 5.3 Good
Resende 1 099.34 283 42 25.7 Average 3.8 Average
Rio Bonito 459.46 32 5 7.0 Poor 1.1 Poor
Rio Claro 846.80 719 74 84.9 Good 8.7 Good
Rio das Flores 478.78 10 2 2.1 Poor 0.4 Poor
Rio das Ostras 228.04 81 7 35.5 Average 3.1 Average
Rio de Janeiro 1 200.33 932 182 77.6 Good 15.2 Good
Santa Maria Madalena 810.96 84 19 10.4 Poor 2.3 Poor
Santo Antônio de Pádua 603.63 47 3 7.8 Poor 0.5 Poor
São Fidélis 1 034.83 308 16 29.8 Average 1.5 Poor
São Francisco de Itabapoana 1 118.04 212 18 19.0 Poor 1.6 Poor
São Gonçalo 248.16 2 1 0.8 Poor 0.4 Poor
São João da Barra 452.40 228 13 50.4 Good 2.9 Average
São João de Meriti 35.22 0 0 0.0 Poor 0.0 Poor
São José de Ubá 249.69 29 3 11.6 Poor 1.2 Poor
São José do Vale do Rio Preto 220.18 12 5 5.5 Poor 2.3 Poor
São Pedro da Aldeia 332.49 22 6 6.6 Poor 1.8 Poor
São Sebastião do Alto 397.21 108 9 27.2 Average 2.3 Poor
Sapucaia 540.67 98 14 18.1 Poor 2.6 Average
Saquarema 352.13 207 34 58.8 Good 9.7 Good
Seropédica 265.19 129 27 48.6 Good 10.2 Good
Silva Jardim 937.76 681 51 72.6 Good 5.4 Good
Sumidouro 413.41 18 3 4.4 Poor 0.7 Poor
Tanguá 143.01 8 2 5.6 Poor 1.4 Poor
Teresópolis 773.34 229 42 29.6 Average 5.4 Good
Trajano de Moraes 591.15 28 5 4.7 Poor 0.8 Poor
Três Rios 322.84 170 16 52.7 Good 5.0 Good
Valença 1 300.77 6 4 0.5 Poor 0.3 Poor
Varre-Sai 201.94 0 0 0.0 Poor 0.0 Poor
Vassouras 536.07 29 3 5.4 Poor 0.6 Poor
Volta Redonda 182.11 14 4 7.7 Poor 2.2 Poor
Rio de Janeiro State 43 750 13 327 1 623 30.5 3.7
Table A2. Abundance of freshwater native fish species per hydrographic region.
Table A2. Abundance of freshwater native fish species per hydrographic region.
RH-1 RH-2 RH-3 RH-4 RH-5 RH-6 RH-7 RH-8 RH-9
Acentronichthys leptos 109 20 0 0 171 17 36 5 7
Ancistrus multispinis 145 119 0 0 501 31 0 5 0
Astyanax keronolepis 1091 97 0 0 551 0 0 0 0
Astyanax lacustris 2 511 81 72 115 184 173 1950 1582
Atlantirivulus janeiroensis 0 0 0 0 0 11 0 0 0
Atlantirivulus jurubatibensis 0 0 0 0 0 0 0 0 51
Atlantirivulus guanabarensis 0 0 0 0 73 0 0 0 0
Atlantirivulus lazzarotoi 17 0 0 0 0 0 0 0 0
Atlantirivulus maricensis 0 0 0 0 1 0 0 0 0
Atlantirivulus simplicis 43 0 0 0 0 0 0 0 0
Australoheros ipatinguensis 0 6 112 99 0 23 32 0 257
Australoheros oblongus 0 18 4 0 89 49 0 126 47
Awaous tajasica 115 5 0 3 81 17 35 113 23
Brachyhypopomus janeiroensis 0 0 0 1 1 82 12 11 49
Brycon insignis 0 0 1 0 0 10 0 7 26
Brycon opalinus 0 30 20 8 0 0 14 0 1
Bryconamericus microcephalus 919 0 0 0 0 0 0 0 0
Bryconamericus ornaticeps 0 287 0 0 1900 0 0 0 0
Bryconamericus tenuis 0 0 0 20 0 128 88 287 40
Callichthys callichthys 0 47 12 3 198 19 11 56 165
Characidium alipioi 0 0 0 21 0 119 45 18 58
Characidium grajahuense 0 140 0 0 321 0 0 0 0
Characidium interruptum 0 14 0 0 249 236 0 38 152
Characidium japuhybense 869 0 0 0 0 0 0 0 0
Characidium lauroi 0 660 135 411 0 0 30 0 0
Characidium litorale 0 0 0 0 0 588 0 172 33
Characidium vidali 0 146 0 124 931 0 10 342 0
Corydoras nattereri 0 63 4 97 314 86 2 7 87
Crenicichla lacustris 0 19 36 7 0 43 60 71 597
Crenicichla lepidota 0 47 0 0 83 2 0 0 0
Cyphocharax gilbert 0 467 1 6 43 344 13 740 381
Delturus parahybae 0 0 0 0 0 0 0 0 1
Deuterodon giton 0 141 154 312 0 2217 243 677 1954
Deuterodon hastatus 193 269 0 32 2620 0 0 0 0
Deuterodon heterostomus 0 4 42 0 2 50 0 1 1
Deuterodon intermedius 179 2574 463 187 0 0 151 0 35
Deuterodon janeiroensis 0 5745 0 7 1384 512 17 0 0
Deuterodon luetkenii 0 0 0 0 35 98 6 6284 1450
Deuterodon taeniatus 0 26 9 70 6 982 346 3072 865
Dormitator maculatus 11 13 0 0 8 1 0 3 80
Eigenmannia virescens 0 0 7 17 7 42 125 14 17
Eleotris pisonis 31 9 0 0 28 26 5 26 23
Geophagus brasiliensis 4092 983 241 346 1809 200 364 875 1114
Glanidium melanopterum 0 14 26 9 0 2 25 3 11
Gymnotus carapo 0 90 37 52 51 44 11 13 23
Gymnotus pantherinus 38 10 0 0 61 47 5 41 5
Harttia carvalhoi 0 88 11 24 0 0 0 0 23
Harttia loricariformis 0 32 13 52 0 0 55 0 7
Hemipsilichthys gobio 0 9 0 10 1 0 8 0 0
Hemipsilichthys nimius 145 0 0 0 0 0 0 0 0
Hemipsilichthys papillatus 0 7 1 0 0 0 0 0 0
Hisonotus notatus 0 205 4 3 689 24 31 0 113
Hisonotus thayeri 0 0 0 0 0 557 92 92 107
Hollandichthys multifasciatus 334 11 0 0 0 0 0 0 0
Homodiaetus banguela 0 0 0 0 0 9 0 0 0
Homodiaetus passarellii 0 2 0 0 48 0 0 0 0
Hoplerythrinus unitaeniatus 0 2 1 0 17 10 0 10 121
Hoplias malabaricus 14 45 13 20 104 83 6 142 163
Hoplosternum littorale 0 96 23 8 38 1 4 2 31
Hyphessobrycon bifasciatus 0 154 8 58 1070 192 11 5063 2894
Hyphessobrycon boulengeri 0 33 0 0 245 15 0 311 496
Hyphessobrycon flammeus 0 48 0 1 35 12 0 0 4
Hypomasticus copelandii 0 10 2 13 13 13 19 19 132
Hypomasticus mormyrops 0 10 14 7 0 0 17 1 3
Hypomasticus thayeri 0 0 0 3 0 0 0 0 21
Hypostomus affinis 0 0 3 0 5 98 1 65 0
Hypostomus auroguttatus 0 0 2 2 0 0 2 0 0
Hypostomus luetkeni 0 34 20 155 0 0 168 0 87
Hypostomus punctatus 0 95 0 0 244 2 0 0 0
Hypostomus vermicularis 0 0 52 4 0 0 2 0 4
Imparfinis minutus 0 61 59 4 0 0 6 0 7
Imparfinis piperatus 0 4 4 0 4 0 0 0 0
Ituglanis parahybae 0 0 0 0 0 10 0 0 3
Jenynsia darwini 0 0 0 0 7 8 0 160 1516
Jenynsia lineata 0 0 0 0 318 65 0 0 0
Knodus moenkhausii 0 0 0 267 0 0 10 0 29
Kronichthys heylandi 664 167 0 1 151 4 0 0 0
Kryptolebias brasiliensis 0 54 0 0 164 16 0 0 0
Kryptolebias caudomarginatus 0 166 0 0 56 0 0 0 0
Kryptolebias gracilis 0 0 0 0 0 28 0 11 0
Kryptolebias ocellatus 0 86 1 0 0 2 0 0 0
Leptolebias marmoratus 0 0 0 0 10 0 0 0 0
Leptopanchax citrinipinnis 184 0 0 0 0
Leptopanchax opalescens 0 7 0 0 55 0 0 0 0
Leptopanchax sanguineus 0 0 0 0 15 0 0 0 0
Leptopanchax splendens 0 0 0 0 50 0 0 0 0
Listrura costai 15 0 0 0 0 0 0 0 0
Listrura nematopteryx 0 0 0 0 156 0 0 0 0
Listrura tetraradiata 0 0 0 0 0 100 0 0 1
Loricariichthys castaneus 0 89 15 1 19 317 19 1 122
Megaleporinus conirostris 0 3 0 4 0 0 23 0 37
Microcambeva barbata 0 0 0 0 0 71 0 4 0
Microcambeva bendego 0 0 0 0 7 0 0 0 0
Microglanis nigripinnis 0 0 0 0 21 0 0 0 0
Microglanis parahybae 0 31 0 0 0 0 44 0 61
Microglanis pleriqueater 0 0 0 0 0 55 0 10 0
Mimagoniates microlepis 808 255 0 637 1328 501 0 30 33
Nematolebias papilliferus 0 0 0 0 28 0 0 0 0
Nematolebias whitei 0 0 0 0 17 291 0 9 0
Neoplecostomus microps 0 825 225 135 82 0 118 219 1
Neoplecostomus paraty 163 0 0 0 0 0 0 0 0
Neoplecostomus variipictus 0 0 0 55 0 0 80 0 0
Notholebias cruzi 0 0 0 0 29 32 0 0 0
Notholebias fractifasciatus 0 0 0 0 20 2 0 0 0
Notholebias minimus 0 181 0 0 8 0 0 0 0
Oligosarcus hepsetus 10 346 94 80 64 24 92 154 249
Ophthalmolebias constanciae 0 0 0 0 0 47 0 65 0
Otocinclus affinis 0 6 1 14 26 3 1 1 13
Otothyris lophophanes 2 24 4 3 45 135 3 69 41
Pachyurus adspersus 0 3 9 1 0 0 7 0 29
Paragenidens grandoculis 0 0 0 0 0 0 0 0 1
Pareiorhaphis garbei 0 0 0 0 232 13 22 54 0
Pareiorhina brachyrhyncha 0 0 160 0 0 0 0 0 0
Pareiorhina rudolphi 77 553 672 0 0 0 0 0 0
Parotocinclus bidentatus 0 0 22 1 0 0 0 0 0
Parotocinclus fluminense 0 0 0 0 0 515 0 0 0
Parotocinclus maculicauda 0 390 0 0 940 36 4 179 0
Parotocinclus muriaensis 0 0 0 0 0 0 0 0 12
Phalloceros anisophallos 3391 1144 0 0 101 0 0 0 0
Phalloceros enneaktinos 701 0 0 0 0 0 0 0 0
Phalloceros harpagos 468 3200 276 1295 773 887 247 3306 1148
Phalloceros leptokeras 946 57 121 2216 1419 56 0 0 0
Phalloceros tupinamba 0 200 0 0 0 0 0 0 0
Phalloptychus januarius 0 33 0 0 1076 133 0 0 11100
Phallotorynus fasciolatus 0 0 0 0 0 0 0 0 47
Piabina argentea 0 0 0 0 0 0 10 0 0
Pimelodella lateristriga 101 58 2 25 224 201 81 167 236
Pimelodus maculatus 0 11 57 11 0 0 8 0 13
Poecilia vivipara 120 378 3 58 2278 298 130 1660 10094
Pogonopoma parahybae 0 0 3 1 0 0 18 0 0
Prochilodus lineatus 0 2 4 0 0 0 13 6 233
Prochilodus vimboides 0 0 0 1 0 0 0 11 170
Psalidodon parahybae 0 138 179 105 82 192 401 9 1395
Pseudotothyris janeirensis 0 147 0 0 29 9 0 29 18
Rhamdia quelen 43 138 24 29 188 26 24 53 55
Rhamdioglanis frenatus 55 72 0 0 48 0 3 0 0
Rhamdioglanis transfasciatus 0 1 0 0 63 108 1 131 0
Rineloricaria nigricauda 0 553 309 381 0 0 4 0 4
Rineloricaria nudipectoris 0 22 0 263 1249 62 0 145 0
Rineloricaria steindachneri 0 3 0 0 0 0 107 0 23
Rineloricaria zawadzki 40 227 14 0 95 0 0 0 0
Schizolecis guentheri 2592 622 0 20 1394 123 42 513 0
Scleromystax barbatus 431 147 0 134 732 144 37 51 7
Scleromystax prionotos 0 0 0 0 0 0 0 11 7
Spintherobolus broccae 0 10 0 0 158 39 0 17 0
Steindachneridion parahybae 0 0 11 4 0 0 1 0 0
Synbranchus marmoratus 3 7 11 2 56 11 4 10 7
Taunayia bifasciata 2 0 1 0 0 0 0 0 0
Trachelyopterus striatulus 0 0 0 0 5 0 7 0 0
Trichogenes longipinnis 627 0 0 0 0 0 0 0 0
Trachelyopterus striatulus 0 31 1 4 0 23 0 71 78
Trichomycterus albinotatus 0 0 94 0 0 0 0 0 0
Trichomycterus auroguttatus 0 11 142 0 0 0 0 0 0
Trichomycterus caipora 0 0 0 0 0 0 0 110 97
Trichomycterus claudiae 0 83 0 0 0 0 0 0 0
Trichomycterus florensis 0 0 16 16 0 0 0 0 0
Trichomycterus fuliginosus 0 0 0 0 0 0 58 0 0
Trichomycterus goeldii 0 0 0 179 0 0 27 0 0
Trichomycterus giganteus 0 201 0 0 153 0 0 0 0
Trichomycterus itatiayae 0 0 197 0 0 0 0 0 0
Trichomycterus jacupiranga 452 30 0 0 77 0 0 0 0
Trichomycterus largoperculatus 0 0 0 33 0 0 28 0 1
Trichomycterus macrophthalmus 0 142 0 0 0 0 0 0 0
Trichomycterus mariamole 0 13 82 0 0 0 0 0 0
Trichomycterus mirissumba 0 0 90 0 0 0 0 0 0
Trichomycterus nigricans 0 0 0 0 972 16 0 0 0
Trichomycterus nigroauratus 0 215 24 0 0 0 0 0 0
Trichomycterus paquequerense 0 0 0 1012 80 0 0 0 0
Trichomycterus potschi 0 134 102 0 0 0 0 0 0
Trichomycterus puriventris 0 0 0 0 0 0 228 0 3
Trichomycterus vitalbrazili 0 0 0 0 0 0 60 0 0

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Figure 1. The nine hydrographic regions along Rio de Janeiro. RH-1- Ilha Grande Bay drainage; RH-2- Rio Guandu and transposition with Paraíba do Sul (08.22e); RH-3- Middle stretch of Rio Paraiba do Sul basin (08.22d); RH-4- Rio Piabanha and tributaries at Paraíba do Sul basin (08.22c); RH-5- Guanabara bay drainage; RH-6- Lake region and Rio São João; RH-7- Rio Dois Rios and tributaries at Paraíba do Sul basin (08.22b); RH-8- Rio Macaé and Rio das Ostras; RH-9- Lower Rio Paraíba do Sul and Itabapoana (08.22a).
Figure 1. The nine hydrographic regions along Rio de Janeiro. RH-1- Ilha Grande Bay drainage; RH-2- Rio Guandu and transposition with Paraíba do Sul (08.22e); RH-3- Middle stretch of Rio Paraiba do Sul basin (08.22d); RH-4- Rio Piabanha and tributaries at Paraíba do Sul basin (08.22c); RH-5- Guanabara bay drainage; RH-6- Lake region and Rio São João; RH-7- Rio Dois Rios and tributaries at Paraíba do Sul basin (08.22b); RH-8- Rio Macaé and Rio das Ostras; RH-9- Lower Rio Paraíba do Sul and Itabapoana (08.22a).
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Figure 2. The twenty-nine river basin divisions along Rio de Janeiro. Coastal drainages: Itabapoana Basin (08.20- Figure 2- A), São Francisco de Itabapoana Watersheds (08.21- Figure 2- B)- , Lagoa Feia Watersheds (08.23- Figure 2- H), Jurubatiba Watersheds (08.24- Figure 2- I), Macaé River Basin (08.25- Figure 2- J), Rio das Ostras Watersheds (08.26- Figure 2- K), São João River Basin (08.27- Figure 2- L), Una River Basin and Búzios Watersheds (08.28- Figure 2- M), Araruama Lagoon Watersheds (08.29- Figure 2- N), Saquarema Watersheds (08.30- Figure 2- O), Maricá Watersheds (08.31), Niterói Watersheds (08.32), Guanabara Bay Watersheds (08.33), Rio de Janeiro Watersheds (08.34), Sepetiba Watersheds (08.35), Mangaratiba Watersheds (08.36), Angra dos Reis Watersheds (08.37), Paraty Watersheds (08.38) and Cairuçu Watersheds (08.39). Rio Paraíba do Sul sections (08.22a to 08.22e).
Figure 2. The twenty-nine river basin divisions along Rio de Janeiro. Coastal drainages: Itabapoana Basin (08.20- Figure 2- A), São Francisco de Itabapoana Watersheds (08.21- Figure 2- B)- , Lagoa Feia Watersheds (08.23- Figure 2- H), Jurubatiba Watersheds (08.24- Figure 2- I), Macaé River Basin (08.25- Figure 2- J), Rio das Ostras Watersheds (08.26- Figure 2- K), São João River Basin (08.27- Figure 2- L), Una River Basin and Búzios Watersheds (08.28- Figure 2- M), Araruama Lagoon Watersheds (08.29- Figure 2- N), Saquarema Watersheds (08.30- Figure 2- O), Maricá Watersheds (08.31), Niterói Watersheds (08.32), Guanabara Bay Watersheds (08.33), Rio de Janeiro Watersheds (08.34), Sepetiba Watersheds (08.35), Mangaratiba Watersheds (08.36), Angra dos Reis Watersheds (08.37), Paraty Watersheds (08.38) and Cairuçu Watersheds (08.39). Rio Paraíba do Sul sections (08.22a to 08.22e).
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Figure 3. Quality of sampling per hydrographic regions RH1 to RH-9. Green = good quality of both indices; yellow = average quality of both indices; red = poor quality of both indices.
Figure 3. Quality of sampling per hydrographic regions RH1 to RH-9. Green = good quality of both indices; yellow = average quality of both indices; red = poor quality of both indices.
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Figure 4. Quality of sampling per municipality. Green = good quality of both indices; yellow = average quality of both indices; red = poor quality of both indices; orange = average number of collection points, good number of lots; pink = good number of collection points, average number of lots; blue = poor number of collection points, average number of lots; gray = good number of collection points, poor number of lots; black = average number of collection points, poor number of lots.
Figure 4. Quality of sampling per municipality. Green = good quality of both indices; yellow = average quality of both indices; red = poor quality of both indices; orange = average number of collection points, good number of lots; pink = good number of collection points, average number of lots; blue = poor number of collection points, average number of lots; gray = good number of collection points, poor number of lots; black = average number of collection points, poor number of lots.
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Figure 5. Temporal variation in the cataloging of fish records in museum collections.
Figure 5. Temporal variation in the cataloging of fish records in museum collections.
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Figure 6. Taxonomic representativeness of fish orders and families for all species (A, B) and freshwater native species (C, D) within the territory of Rio de Janeiro.
Figure 6. Taxonomic representativeness of fish orders and families for all species (A, B) and freshwater native species (C, D) within the territory of Rio de Janeiro.
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Figure 7. Diversity profile using the Rényi series representing the fish fauna documented in the museum collections for the hydrographic regions of Rio de Janeiro. A. Diversity profile between RH-1 and RH-2. B. Profile between RH-1 and RH-9. C. Profile between RH-3, RH-4 and RH-7. D. Profile between RH-5, RH-6 and RH-8.
Figure 7. Diversity profile using the Rényi series representing the fish fauna documented in the museum collections for the hydrographic regions of Rio de Janeiro. A. Diversity profile between RH-1 and RH-2. B. Profile between RH-1 and RH-9. C. Profile between RH-3, RH-4 and RH-7. D. Profile between RH-5, RH-6 and RH-8.
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Table 1. Lots sampled in the state of Rio de Janeiro.
Table 1. Lots sampled in the state of Rio de Janeiro.
Ocean Continental Not identified %
Marine origin 2,890 985 ----- 7.39%
Allochthonous freshwater ----- 498 ----- 3.74%
Native freshwater ----- 11,724 ----- 87.97%
Not identified at the species level ----- 120 ----- 0.90%
Not identified at the locality level ----- ----- 82
Total 2,890 13,327 82 16,299
% 17.73% 81.77% 0.50%
Table 2. Quality of sampling per hydrographic region.
Table 2. Quality of sampling per hydrographic region.
Code Hydrographic region Area (km2) Lots Points il ilq ip ipq
RH-1 Baía da Ilha Grande 1,919.1 1,150 140 59.9 Good 7.3 Good
RH-2 Guandu 4,087.8 1,789 234 43.8 Good 5.7 Good
RH-3 Middle Paraíba do Sul 7,114.1 860 133 12.1 Poor 1.9 Poor
RH-4 Piabanha 3,831.4 816 121 21.3 Average 3.2 Average
RH-5 Baía de Guanabara 696.0 3,190 428 458.3 Good 61.5 Good
RH-6 Lagos São João 4,030.2 1,353 143 33.6 Average 3.5 Average
RH-7 Rio Dois Rios 4,940.9 780 81 15.8 Poor 1.6 Poor
RH-8 Macaé and das Ostras 2,226.9 1,474 133 66.2 Good 6.0 Good
RH-9 Lower Paraíba do Sul and Itabapoana 14,904.0 1,915 210 12.8 Poor 1.4 Poor
Rio de Janeiro State 43,750 13,327 1.623 30.5 3.7
Table 3. Sample quality per basin groups.
Table 3. Sample quality per basin groups.
Code Basin groups Area (km2) Lots Points Il Ilq Ip Ipq
08.20 Itabapoana 1,523.4 122 19 8.0 Poor 1.2 Poor
08.21 São Francisco do Itabapoana 971.3 38 6 3.9 Poor 0.6 Poor
08.22a Rio Paraíba do Sul - RH-9 6,321.4 1012 63 16.0 Poor 1.0 Poor
08.22b Rio Paraíba do Sul - RH-7 4,468.6 780 81 17.5 Poor 1.8 Poor
08.22c Rio Paraíba do Sul - RH-4 3,469.0 816 121 23.5 Average 3.5 Average
08.22d Rio Paraíba do Sul - RH-3 6,430.8 860 133 13.4 Poor 2.1 Poor
08.22e Rio Paraíba do Sul - RH-2 1,014.1 611 68 60.3 Good 6.7 Good
08.23 Lagoa Feia 4,310.8 512 93 11.9 Poor 2.2 Poor
08.24 Jurubatiba 410.1 707 62 172.4 Good 15.1 Good
08.25 Rio Macaé 1,706.4 894 92 52.4 Good 5.4 Good
08.26 Rio das Ostras 249.4 104 8 41.7 Good 3.2 Average
08.27 Rio São João 2,155.6 1052 86 48.8 Good 4.0 Average
08.28 Rio Una and Búzios 541.7 11 4 2.0 Poor 0.7 Poor
08.29 Lagoa de Araruama 677.5 67 16 9.9 Poor 2.4 Poor
08.30 Saquarema 265.2 210 35 79.2 Good 13.2 Good
08.31 Maricá 349.5 167 33 47.8 Good 9.4 Good
08.32 Niterói 51.5 23 6 44.7 Good 11.7 Good
08.33 Baía de Guanabara 4,073.8 2412 269 59.2 Good 6.6 Good
08.34 Rio de Janeiro 2,636.3 1673 269 63.5 Good 10.2 Good
08.35 Sepetiba 107.0 39 7 36.4 Average 6.5 Good
08.36 Mangaratiba 289.8 67 12 23.1 Average 4.1 Average
08.37 Angra dos Reis 1,028.2 557 59 54.2 Average 5.7 Average
08.38 Paraty 376.9 301 48 79.9 Good 12.7 Good
08.38 Cairuçu 322.2 292 33 90.6 Good 10.2 Good
Rio de Janeiro State 43,750 13,327 1,623 30.5 3.7
Table 4. Number of fish species sampled in inland waters in the state of Rio de Janeiro.
Table 4. Number of fish species sampled in inland waters in the state of Rio de Janeiro.
Environment/Origin S %
Native freshwater 172 49.28%
Allochthonous freshwater 22 6.30%
Marine origin 152 43.55%
Not identified at the species level 3 0.86%
Total 349
Table 5. Inventory of native freshwater species recorded for Rio de Janeiro state.
Table 5. Inventory of native freshwater species recorded for Rio de Janeiro state.
Order Family Subfamily Suggested species Author
Gymnotiformes Sternopygidae Eigenmannia virescens (Valenciennes, 1836)
Gymnotidae Gymnotinae Gymnotus carapo Linnaeus, 1758
Gymnotus pantherinus (Steindachner, 1908)
Hypopomidae Brachyhypopomus janeiroensis (Costa & Campos-da-Paz, 1992)
Characiformes Crenuchidae Characidiinae Characidium alipioi Travassos, 1955
Characidium grajahuense Travassos, 1944
Characidium interruptum Pellegrin, 1909
Characidium japuhybense Travassos, 1949
Characidium lauroi Travassos, 1949
Characidium litorale Leitão & Buckup, 2014
Characidium vidali Travassos, 1967
Erythrinidae Hoplerythrinus unitaeniatus (Spix & Agassiz, 1829)
Hoplias malabaricus (Bloch, 1794)
Anostomidae Hypomasticus copelandii (Steindachner, 1875)
Hypomasticus mormyrops (Steindachner, 1875)
Hypomasticus thayeri (Borodin, 1929)
Megaleporinus conirostris (Steindachner, 1875)
Curimatidae Cyphocharax gilbert (Quoy & Gaimard, 1824)
Prochilodontidae Prochilodus lineatus (Valenciennes, 1837)
Prochilodus vimboides Kner, 1859
Bryconidae Bryconinae Brycon insignis Steindachner, 1877
Brycon opalinus (Cuvier, 1819)
Characidae Stethaprioninae Astyanax keronolepis Silva, Malabarba & Malabarba, 2019
Astyanax lacustris (Lütken, 1875)
Deuterodon giton (Eigenmann, 1908)
Deuterodon hastatus (Myers, 1928)
Deuterodon heterostomus (Eigenmann, 1911)
Deuterodon intermedius (Eigenmann, 1908)
Deuterodon janeiroensis (Eigenmann, 1908)
Deuterodon luetkenii (Boulenger, 1887)
Deuterodon taeniatus (Jenyns, 1842)
Hollandichthys multifasciatus (Eigenmann & Norris, 1900)
Hyphessobrycon bifasciatus Ellis, 1911
Hyphessobrycon boulengeri (Eigenmann, 1907)
Hyphessobrycon flammeus Myers, 1924
Oligosarcus hepsetus (Cuvier, 1829)
Psalidodon parahybae (Eigenmann, 1908)
Spintherobolinae Spintherobolus broccae Myers, 1925
Stevardiinae Bryconamericus microcephalus (Miranda Ribeiro, 1908)
Bryconamericus ornaticeps Bizerril & Perez-Neto, 1995
Bryconamericus tenuis Bizerril & Auraujo, 1992
Knodus moenkhausii (Eigenmann & Kennedy, 1903)
Mimagoniates microlepis (Steindachner, 1877)
Piabina argentea Reinhardt, 1867
Siluriformes Trichomycteridae Trichogeninae Trichogenes longipinnis Britski & Ortega, 1983
Trichomycterinae Trichomycterus albinotatus Costa, 1992
Ituglanis parahybae (Eigenmann, 1918)
Trichomycterus auroguttatus Costa, 1992
Trichomycterus caipora Lima, Lazzarotto & Costa, 2008
Trichomycterus claudiae Barbosa & Costa, 2010
Trichomycterus florensis (Miranda-Ribeiro, 1943)
Trichomycterus fuliginosus Barbosa & Costa, 2010
Trichomycterus giganteus Lima & Costa, 2004
Trichomycterus goeldii Boulenger, 1896
Trichomycterus itatiayae Miranda Ribeiro, 1906
Trichomycterus jacupiranga Wosiacki & Oyakawa, 2005
Trichomycterus largoperculatus Costa & Katz, 2022
Trichomycterus macrophthalmus Barbosa & Costa, 2012
Trichomycterus mariamole Barbosa & Costa, 2010
Trichomycterus mirissumba Costa, 1992
Trichomycterus nigricans Valenciennes, 1832
Trichomycterus nigroauratus Barbosa & Costa, 2008
Trichomycterus paquequerense (Miranda Ribeiro, 1943)
Trichomycterus potschi Barbosa & Costa, 2003
Trichomycterus puriventris Barbosa & Costa, 2012
Trichomycterus travassosi (Miranda Ribeiro, 1949
Trichomycterus vitalbrazili Vilardo, Katz & Costa, 2020
Microcambevinae Listrura costai Villa-Verde, Lazzarotto & Lima, 2012
Listrura nematopteryx de Pinna, 1988
Listrura tetraradiata Landim & Costa, 2002
Microcambeva barbata Costa & Bockmann, 1994
Microcambeva bendego Medeiros, Moreira, de Pinna & Lima, 2020
Stegophilinae Homodiaetus banguela Koch, 2002
Homodiaetus passarellii (Miranda Ribeiro, 1944
Callichthyidae Callichthyinae Callichthys callichthys (Linnaeus, 1758)
Hoplosternum littorale (Hancock, 1828)
Corydoradinae Corydoras nattereri Steindachner, 1876
Scleromystax barbatus (Quoy & Gaimard, 1824)
Scleromystax prionotos (Nijssen & Isbrücker, 1980)
Loricariidae Delturinae Delturus parahybae Eigenmann & Eigenmann, 1889
Hemipsilichthys gobio (Lütken, 1874)
Hemipsilichthys nimius Pereira, Reis, Souza & Lazzarotto, 2003
Hemipsilichthys papillatus Pereira, Oliveira & Oyakawa, 2000
Rhinelepinae Pogonopoma parahybae (Steindachner, 1877)
Loricariinae Harttia carvalhoi Miranda Ribeiro, 1939
Harttia loricariformis Steindachner, 1877
Loricariichthys castaneus (Castelnau, 1855)
Rineloricaria nigricauda (Regan, 1904)
Rineloricaria nudipectoris Mejia, Ferrar & Buckup, 2023
Rineloricaria steindachneri (Regan, 1904)
Rineloricaria zawadzki Silva, Costa & Oliveira, 2022
Hypoptopomatinae Hisonotus notatus Eigenmann & Eigenmann, 1889
Hisonotus thayeri Martins & Langeani, 2016
Kronichthys heylandi (Boulenger, 1900)
Neoplecostomus microps (Steindachner, 1877)
Neoplecostomus paraty Cherobim, Lazzarotto & Langeani, 2016
Neoplecostomus variipictus Bizerril, 1995
Otocinclus affinis Steindachner, 1877
Otothyris lophophanes (Eigenmann & Eigenmann, 1889)
Pareiorhaphis garbei (Ihering, 1911)
Pareiorhina brachyrhyncha Chamon, Aranda & Buckup, 2005
Pareiorhina rudolphi (Miranda Ribeiro, 1911)
Parotocinclus bidentatus Gauger & Buckup, 2005
Parotocinclus fluminense Roxo, Melo, Silva & Oliveira, 2017
Parotocinclus maculicauda (Steindachner, 1877)
Parotocinclus muriaensis Gauger & Buckup, 2005
Pseudotothyris janeirensis Britski & Garavello, 1984
Schizolecis guentheri (Miranda Ribeiro, 1918)
Hypostominae Ancistrus multispinis (Regan, 1912)
Hypostomus affinis (Steindachner, 1877)
Hypostomus auroguttatus Kner, 1854
Hypostomus luetkeni (Steindachner, 1877)
Hypostomus punctatus Valenciennes, 1840
Hypostomus vermicularis (Eigenmann & Eigenmann, 1888)
Auchenipteridae Centromochlinae Glanidium melanopterum Miranda Ribeiro, 1918
Auchenipterinae Trachelyopterus striatulus (Steindachner, 1877)
Heptapteridae Rhamdiinae Pimelodella lateristriga (Lichtenstein, 1823)
Rhamdia quelen (Quoy & Gaimard, 1824)
Heptapterinae Acentronichthys leptos Eigenmann & Eigenmann, 1889
Imparfinis minutus (Lütken, 1874)
Imparfinis piperatus Eigenmann & Norris, 1900
Rhamdioglanis frenatus Ihering, 1907
Rhamdioglanis transfasciatus Miranda Ribeiro, 1908
Taunayia bifasciata (Eigenmann & Norris, 1900)
Pimelodidae Pimelodus maculatus Lacepède, 1803
Steindachneridion parahybae (Steindachner, 1877)
Pseudopimelodidae Microglanis nigripinnis Bizerril & Perez-Neto, 1992
Microglanis parahybae (Steindachner, 1880)
Microglanis pleriqueater Mattos, Ottoni & Barbosa, 2013
Ariidae Ariinae Paragenidens grandoculis (Steindachner, 1877)
Gobiiformes Eleotridae Eleotrinae Dormitator maculatus (Bloch, 1792)
Eleotris pisonis (Gmelin, 1789)
Oxudercidae Gobionellinae Awaous tajasica (Lichtenstein, 1822)
Synbranchiformes Synbranchidae Synbranchus marmoratus Bloch, 1795
Cyprinodontiformes Rivulidae Rivulinae Atlantirivulus guanabarensis Costa, 2014
Atlantirivulus janeiroensis (Costa, 1991)
Atlantirivulus jurubatibensis (Costa, 2008)
Atlantirivulus lazzarotoi (Costa, 2007)
Atlantirivulus maricensis Costa, 2014
Atlantirivulus simplicis (Costa, 2004)
Kryptolebiatinae Kryptolebias brasiliensis (Valenciennes, 1821)
Kryptolebias caudomarginatus (Seegers, 1984)
Kryptolebias gracilis Costa, 2007
Kryptolebias ocellatus (Hensel, 1868)
Cynolebiinae Leptolebias marmoratus (Ladiges, 1934)
Leptopanchax citrinipinnis (Costa, Lacerda & Tanizaki, 1988)
Leptopanchax opalescens (Myers, 1942)
Leptopanchax sanguineus Costa, 2019
Leptopanchax splendens (Myers, 1942)
Nematolebias papilliferus Costa, 2002
Nematolebias whitei (Myers, 1942)
Notholebias cruzi (Costa, 1988)
Notholebias fractifasciatus (Costa, 1988)
Notholebias minimus (Myers, 1942)
Ophthalmolebias constanciae (Myers, 1942)
Poeciliidae Poeciliinae Phalloceros anisophallos Lucinda, 2008
Phalloceros enneaktinos Lucinda, 2008
Phalloceros harpagos Lucinda, 2008
Phalloceros leptokeras Lucinda, 2008
Phalloceros tupinamba Lucinda, 2008
Phalloptychus januarius (Hensel, 1868)
Phallotorynus fasciolatus Henn, 1916
Poecilia reticulata Peters, 1859
Poecilia vivipara Bloch & Schneider, 1801
Anablepidae Anablepinae Jenynsia darwini Amorim, 2018
Jenynsia lineata (Jenyns, 1842)
Cichliformes Cichlidae Cichlinae Australoheros ipatinguensis Ottoni & Costa, 200
Australoheros oblongus (Castelnau 1855)
Crenicichla lacustris (Castelnau, 1855)
Crenicichla lepidota Heckel, 1840
Geophagus brasiliensis (Quoy & Gaimard, 1824)
Acanthuriformes Sciaenidae Pachyurus adspersus Steindachner, 1879
Table 6. Parametric and non-parametric diversity estimation indices per hydrographic region in the state of Rio de Janeiro.
Table 6. Parametric and non-parametric diversity estimation indices per hydrographic region in the state of Rio de Janeiro.
RH-1 RH-2 RH-3 RH-4 RH-5 RH-6 RH-7 RH-8 RH-9
Taxa_S 40 97 67 69 89 80 74 67 81
Individuals 20058 24736 4581 9256 29430 11829 4243 27928 40232
Dominance_D 0.1032 0.0915 0.05703 0.1038 0.04186 0.06386 0.04392 0.1214 0.1549
Shannon_H 2.7 3.176 3.284 2.858 3.528 3.366 3.516 2.595 2.513
Equitability_J 0.732 0.6944 0.7811 0.675 0.786 0.7682 0.8169 0.6172 0.5719
Chao-1 40 97.17 74 78.33 90.5 80.17 75.5 70 102
iChao-1 40 97.17 78.57 86.75 93.5 80.4 77.07 76.5 103.7
ACE 40 97.3 73.45 74.2 90.16 81.2 75.69 68.96 85.34
Squares 40 97.09 70.66 75.65 89.38 80.26 74.71 68.94 88.59
Variation of species richness for the non-parametric estimation indices
RH-1 RH-2 RH-3 RH-4 RH-5 RH-6 RH-7 RH-8 RH-9
Chao-1 0.00% 0.18% 10.45% 13.52% 1.69% 0.21% 2.03% 4.48% 25.93%
iChao-1 0.00% 0.18% 17.27% 25.72% 5.06% 0.50% 4.15% 14.18% 28.02%
ACE 0.00% 0.31% 9.63% 7.54% 1.30% 1.50% 2.28% 2.93% 5.36%
Squares 0.00% 0.09% 5.46% 9.64% 0.43% 0.32% 0.96% 2.90% 9.37%
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