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

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 provide a checklist of species, to identify patterns of diversity and the distribution of freshwater ichthyofauna, to delineate biogeographic units, and to explore changes in faunal composition among different areas. Analyzing data from ichthyological collections and the literature on original species descriptions revealed 206 freshwater fish species: 183 native and 23 allochthonous. The checklist includes updated species names. The sampling effort in Rio de Janeiro is extensive, especially in coastal lowlands. The findings indicate that inventory work is still needed in some areas, particularly within the Rio Paraíba do Sul basin. Seven bioregions of freshwater ichthyofauna were identified, including a major region of higher species richness and smaller areas with higher endemism of restricted-range species. This biogeographic assessment underscores the diverse and distinctive freshwater fish fauna in the basins of Rio de Janeiro, with well-defined biogeographic units.

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. 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 in its 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 eastern and northern coastal regions, lake systems dominate the landscape, with many lagoons, such as Maricá, Saquarema, Araruama, and Feia [1]. The significant variety of environments, combined with the complex geomorphological history of this region, have driven the evolution of a rich and diverse biota, encompassing both forest and adjacent aquatic systems.

The fish fauna of Rio de Janeiro has been extensively studied, initially by foreign naturalists, such as Johann von Spix, Johann Natterer, Carl von Martius, Louis Agassiz, Francis de Castelnau, Jean René Quoy, Joseph Gaimard, Charles Hartt, Edward Copeland, John Hasemann, and George Myers, and further in the studies of the Brazilian naturalists Alípio de Miranda Ribeiro, Paulo de Miranda Ribeiro, and Haroldo Travassos up until the mid-20th century [1]. With the establishment of the Museu Nacional as a referential research center, holding one of the largest ichthyological collections in Latin America, along with other institutions established later, ichthyological research in the state of Rio de Janeiro increased considerably. Numerous studies have been conducted, including taxonomic papers (e.g., [2,3,4,5,6,7,8,9,10,11,12,13]), inventories (e.g., [1,14,15,16,17,18,19,20,21]), and studies in ecology and other fields (e.g., [22,23,24,25,26,27,28,29,30]). The collective effort of many researchers over three centuries resulted in the creation of a robust database of species records and environmental data in museums and university collections.

However, there is a gap of comprehensive and cohesive 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. Regional species lists are important to identify priority areas for future studies, to point out taxonomic gaps and putatively undescribed species, and to generate estimates on the number of existing species and their geographic distributions [31]. In this context, this study highlights the rivers of Rio de Janeiro, identifies the respective basins and fishes in a territorial context, and provides the first comprehensive framework of spatial biodiversity patterns for the freshwater fish fauna of the state.

2. Materials and Methods

2.1. Study Area

The state of Rio de Janeiro covers an area of approximately 43,750 km², inserted in the Atlantic Forest biome and presenting a great diversity of phytophysiognomies, climates, and relief, in addition to being home to the second highest population density in the country [32]. The relief of the state is intersected by mountain ranges that generate and divide the hydrographic basins of this region (Figure 1). The state is bordered in its northern and western regions by the Paraíba do Sul River and in part by the Serra da Mantiqueira, which separates the middle and lower stretches of the Paraíba do Sul River basin from the Upper Rio Paraná basin, in São Paulo, and the Rio Doce basin, in Minas Gerais. The Rio Paraíba do Sul basin is separated from the other coastal basins of Rio de Janeiro by the Serra do Mar, a crystalline formation from which several Atlantic drainages emerge that cut through the state, flowing into lowland areas [33].

Rio de Janeiro is part of the Southeast Atlantic Hydrographic Region, comprising river basins flowing into the Atlantic Ocean on southeastern portion of the country. The hydrographic area extends from the Rio Doce basin, in the states of Espírito Santo and Minas Gerais, to the Rio Ribeira do Iguape basin, in the state of Paraná. The hydrographic region is bordered to the west by the hydrographic regions of the São Francisco and Paraná. Each river basin in Rio de Janeiro is identified by a number between 20 and 39.

The state of Rio de Janeiro is divided into nine hydrographic regions—RH-1 to RH-9—representing a political division for territorial governance (Figure 2A). The river basins within Rio de Janeiro are categorized into 24 groups (Figure 2B). Of these, 5 belong to the Rio Paraíba do Sul basin, while 19 are coastal basins and micro basins located on the eastern slope of the Serra do Mar and within coastal lagoon systems (Figure 2B).

2.2. Species Data

Available records from fish collections in Rio de Janeiro were reviewed and their identifications confirmed. The coordinates for each sampling point were estimated from the locations provided in the records. Initially, a curatorial survey of data was conducted using the fish collections at the Museu Nacional (MNRJ) and the Museu de Biologia Mello Leitão (MBML). Additionally, records from the SpeciesLink database at the Centro de Referência em Informação Ambiental (CRIA) were consulted, along with the literature on species descriptions. Data were compiled from the ichthyological collections of AMNH, ANSP, BMHN, BMNH, CAGC, CAS (including CAS-SU), CICCAA, DZSJRP, FMNH, INPA, MCP, MCZ, MHNG, MTD, MZUEL, MZUSP, NPM, NRM, UF, UFMT, UFRGS, UFRJ, UFRN, UMMZ, UMZC, UNT, USNM, ZFMK, ZMH, and ZUEC (Supplementary File—Table S1). Institutional acronyms are detailed in [34]. The records were georeferenced and plotted on a map of the state of Rio de Janeiro to ensure their accuracy. A total of 13,585 lots were inventoried (Supplementary File—Table S2). Taxonomic classification follows [35].

Some species discussed here have complex taxonomy with unresolved phylogenies, and possibly represent species complexes. For analytical and inventory purposes, these species are considered as single taxa. 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 [36]. Ref. [37] describes seven subspecies of Gymnotus carapo for South American basins and suggests 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 in museum collections and databases. Hoplias malabaricus and Eigenmannia virescens likely represent species complexes that require revision [38,39,40]. Additionally, we adopt the name Synbranchus sp. as recommended by group specialists ([41], T. Roberts, pers. comm.).

2.3. Geographic Data

In the process of georeferencing and verifying the accuracy of coordinates for each sampling point, the originally provided values were initially used. When a small discrepancy was found between the reported value and the indicated locality data, the value was adjusted according to the locality. In cases where there was a significant discrepancy between the indicated coordinates and the locality, or when the coordinates were unavailable, the coordinates were estimated based on the available information.

The hydrographic maps were adapted for use in TrackMaker software version 5.1 [42], starting with the 1:25,000 resolution provided by the Instituto Brasileiro de Geografia e Estatística (IBGE) [43]. For suggested locality data (in the absence or discrepancy of provided coordinates), information was used according to the river names in this version, supplemented by names from the IBGE 1:50,000 topographic maps. Municipality areas were calculated using IBGE data [44].

2.4. Sampling Coverage Assessment

The sampling coverage in the state was assessed by calculating the total lots index (il) and the sampling points index (ip) per 100 km². These indices were computed for each municipality, hydrographic region, and group of basins, and the results were compared with the index for the entire state [45]. Sampling quality was considered average when within about 30%, poor when significantly below 30%, and good when above 30% (for both il and ip). We assessed the sampling quality for the nine hydrographic regions, twenty-nine river basin divisions, and by municipality.

2.5. Biogeographic and Diversity Patterns

We applied the constancy index [46] to determine which species are constant over time, calculated as C = n/N × 100, where n is the number of collection points where the species was captured and N = total number of collection points. Based on the results obtained, each species was classified as follows: constant if C > 50%; accessory if C ranges between 25% and 50%; and accidental if C < 25%.

The diversity patterns of each hydrographic region (alpha-diversity) were assessed by calculating the indices of absolute richness, Shannon diversity, equitability, and dominance, using species abundance data for each region [47]. To evaluate differences between communities (beta-diversity or species turnover) among hydrographic regions, we applied non-metric multidimensional scaling (NMDS) using a binary matrix and the Jaccard dissimilarity index, with 100 trials. These analyses were conducted using PAST software version 4.17 [48].

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 observed richness and estimating total richness from an incomplete sample of a biological community [49]. Consequently, we employed several diversity estimators to assess the completeness of species sampling for the hydrographic regions, as follows: the Chao 1 index: A simple estimator of the absolute number of species in a community, based on the number of rare species within a sample [50,51]; the iChao index: An improved version of the Chao 1 index, offering greater precision in the evaluation of results [52]; the AC estimator (abundance-based coverage estimator): This method uses the abundance of rare species (i.e., species with low abundance) [53,54]. Unlike the previous estimators, the AC estimator allows for setting thresholds to define what constitutes a rare species. Generally, species with an abundance of 1 to 10 individuals are considered rare, and the estimated richness can vary depending on the abundance threshold chosen. Unfortunately, there are no defined biological criteria for selecting the optimal range. Additionally, we used the squares index, a richness estimator designed to be more accurate than Chao 1 when abundance distributions are uniform [55].

Only records of native freshwater species were considered for the following spatial assessments. We executed a data cleaning routine using the Coordinate Cleaner package [56] in the R software v. 4.3 [57], removing duplicated coordinates for each species, to avoid pseudoreplication, which can produce distorted results. A bioregionalization analysis was performed using the Infomap Bioregions 2 algorithm [58], to subdivide the state of Rio de Janeiro into smaller biogeographical units that reflect the distribution patterns of species. 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. A wide range of scaling parameters were tested, and the most consistent results were obtained with the following scenario: cell size ranging from 1/16° to 1/2°, cell capacity ranging from 1 to 300 samples, and 100 trials. The remaining settings followed the program defaults. A cluster graph using the binary matrix resulting from the bioregionalization algorithm (inspected to correct possible inconsistencies in the distribution of species) was produced to verify the similarity in faunal compositions among the bioregions generated, employing the UPGMA algorithm and Jaccard’s similarity coefficient.

A species richness interpolation (SRI) 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 [59] of the Dinamica-EGO software v. 7.8 [60]. The following parameters were used: kriging interpolation model = Gaussian, raster grid size = 0.02, hexagon size = 0.2 (20 km), and a minimum of one sample per hexagon.

We applied the weight endemism index (WEI), which combines endemism and species richness to identify areas with high endemicity of restricted-range species [61,62]. The following parameters were used: kriging interpolation model = Gaussian, raster grid size = 0.02, hexagon size = 0.2 (20 km), and a minimum of one sample per hexagon, in the BioDinamica model [59].

3. Results

The freshwater environments of Rio de Janeiro encompass 206 species (183 native and 23 allochthonous) (

Table A2. Inventory of native freshwater species recorded for the state of Rio de Janeiro. Taxonomic nomenclature of fishes by family follows [35]). Allochthonous and exotic species indicated by an asterisk *. Species in bold are endemic to Rio de Janeiro state.

Taxon	Author	Record in RJ
CHARACIFORMES
Crenuchidae
Characidiinae
Characidium alipioi	Travassos 1955	[1,70]
Characidium grajahuense	Travassos 1944	[1,21]
Characidium interruptum	Pellegrin 1909	[14,25,29]
Characidium japuhybense	Travassos 1949	[21]
Characidium lauroi	Travassos 1949	[1,17,19]
Characidium litorale	Leitão & Buckup 2014	[9]
Characidium vidali	Travassos 1967	[1,18,19,29]
Erythrinidae
Hoplerythrinus unitaeniatus	(Spix & Agassiz 1829)	[1,14,16]
Hoplias lacerdae *	Miranda Ribeiro 1908	[1]
Hoplias malabaricus	(Bloch 1794)	[1,14,16,21]
Parodontidae
Apareiodon piracicabae	(Eigenmann 1907)	MNRJ 45942
Serrasalmidae
Colossomatinae
Colossoma macropomum *	(Cuvier 1816)	[1]
Serrasalminae
Metynnis lippincottianus *	(Cope 1870)	MNRJ 49310
Metynnis maculatus *	(Kner 1858)	[16,27,86]
Anostomidae
Hypomasticus copelandii	(Steindachner 1875)	[27,86] as Leporinus copelandii
Hypomasticus mormyrops	(Steindachner 1875)	[19,27,86] as Leporinus mormyrops
Hypomasticus thayeri	(Borodin 1929)	[33] as Leporinus cf. thayeri
Megaleporinus conirostris	(Steindachner 1875)	[1,27,86] as Leporinus conirostris
Curimatidae
Cyphocharax gilbert	(Quoy & Gaimard 1824)	[1,16,27]
Prochilodontidae
Prochilodus lineatus	(Valenciennes 1837)	[1,27]
Prochilodus vimboides	Kner 1859	[1,27]
Lebiasinidae
Pyrrhulininae
Nannostomus beckfordi *	Günther 1872	[1] as N. brechforti
Pyrrhulina australis *	Eigenmann & Kennedy 1903	MNRJ 50781
Pyrrhulina filamentosa *	Valenciennes 1847	MNRJ 14996
Bryconidae
Bryconinae
Brycon insignis	Steindachner 1877	[27,86]
Brycon opalinus	(Cuvier 1819)	[19,30]
Salmininae
Salminus brasiliensis *	(Cuvier 1816)	[27]
Characidae
Stethaprioninae
Astyanax jenynsii	(Steindachner 1877)	[67,87]
Astyanax keronolepis	Silva, Malabarba & Malabarba 2019	[10]
Astyanax lacustris	(Lütken 1875)	[21]
Astyanax viridis	Salgado 2021	[79]
Deuterodon giton	(Eigenmann 1908)	[1,14,18,29] as Astyanax giton
Deuterodon hastatus	(Myers 1928)	[20,21,25,29] as Astyanax hastatus
Deuterodon heterostomus	(Eigenmann 1911)	[27,86] as Probolodus heterostomus
Deuterodon intermedius	(Eigenmann 1908)	[1,17] as Astyanax intermedius, [20,21]
Deuterodon janeiroensis	(Eigenmann 1908)	[1,15,25,29] as Astyanax janeiroensis, [20,21]
Deuterodon luetkenii	(Boulenger 1887)	[16] as Hyphessobrycon luetkenii
Deuterodon taeniatus	(Jenyns 1842)	[1,14,15,29] as Astyanax taeniatus
Hollandichthys multifasciatus	(Eigenmann & Norris 1900)	[20,21]
Hyphessobrycon bifasciatus	Ellis 1911	[15,16,25]
Hyphessobrycon eques *	(Steindachner 1882)	[27]
Hyphessobrycon flammeus	Myers 1924	[1,70,88]
Hyphessobrycon reticulatus	Ellis 1911	[14,16,18,25]
Oligosarcus acutirostris	Menezes 1990	MCZ 20605
Oligosarcus hepsetus	(Cuvier 1829)	[16,19,21,27]
Psalidodon parahybae	(Eigenmann 1908)	[27,67,86] as Astyanax parahybae
Spintherobolinae
Spintherobolus broccae	Myers 1925	[1,70]
Aphyocharacinae
Aphyocharax anisitsi *	Eigenmann & Kennedy 1903	MNRJ 5585
Stevardiinae
Bryconamericus microcephalus	(Miranda Ribeiro 1908)	[1,29]
Bryconamericus ornaticeps	Bizerril & Perez-Neto 1995	[20,21,29]
Bryconamericus tenuis	Bizerril & Auraujo 1992	[1,29]
Knodus moenkhausii *	(Eigenmann & Kennedy 1903)	MNRJ 43351
Mimagoniates microlepis	(Steindachner 1877)	[14,25,29]
Piabina argentea	Reinhardt 1867	[89]
Pseudocorynopoma doriae *	Perugia 1891	USNM 129920
SILURIFORMES
Trichomycteridae
Trichogeninae
Trichogenes longipinnis	Britski & Ortega 1983	[20,21,90,91]
Trichomycterinae
Ituglanis parahybae	(Eigenmann 1918)	[1,92]
Trichomycterus albinotatus	Costa 1992	[1,70]
Trichomycterus auroguttatus	Costa 1992	[1,70]
Trichomycterus caipora	Lima, Lazzarotto & Costa 2008	[93,94]
Trichomycterus claudiae	Barbosa & Costa 2010	[7]
Trichomycterus florensis	(Miranda-Ribeiro 1943)	[1,94]
Trichomycterus fuliginosus	Barbosa & Costa 2010	[7]
Trichomycterus giganteus	Lima & Costa 2004	[95]
Trichomycterus goeldii	Boulenger 1896	[1]
Trichomycterus itatiayae	Miranda Ribeiro 1906	[1,96]
Trichomycterus jacupiranga	Wosiacki & Oyakawa 2005	[21]
Trichomycterus largoperculatus	Costa & Katz 2022	[97]
Trichomycterus macrophthalmus	Barbosa & Costa 2012	[17,19,98]
Trichomycterus mariamole	Barbosa & Costa 2010	[7,17,19]
Trichomycterus mirissumba	Costa 1992	[1,7]
Trichomycterus nigricans	Valenciennes 1832	[1,94]
Trichomycterus nigroauratus	Barbosa & Costa 2008	[17,19,96]
Trichomycterus paquequerensis	(Miranda Ribeiro 1943)	[1,29,94]
Trichomycterus potschi	Barbosa & Costa 2003	[21,99]
Trichomycterus puriventris	Barbosa & Costa 2012	[100]
Trichomycterus quintus	Costa 2020	[101]
Trichomycterus santaeritae	(Eigenmann 1918)	[94]
Trichomycterus saquarema	Costa, Katz, Vilardo & Amorim 2022	[102]
Trichomycterus travassosi	(Miranda Ribeiro 1949)	[1,70]
Trichomycterus vitalbrazili	Vilardo, Katz & Costa 2020	[103]
Microcambevinae
Listrura costai	Villa-Verde, Lazzarotto & Lima 2012	[21,104,105]
Listrura macacuensis	Costa & Katz 2021	[104,105]
Listrura macaensis	Costa & Katz 2021	[104,105]
Listrura menezesi	Villa-Verde, de Pinna, Reis & Oyakawa 2022	[105,106]
Listrura nematopteryx	de Pinna 1988	[29,104,105]
Listrura tetraradiata	Landim & Costa 2002	[104,105]
Microcambeva barbata	Costa & Bockmann 1994	[11,107]
Microcambeva bendego	Medeiros, Moreira, de Pinna & Lima 2020	[11]
Stegophilinae
Homodiaetus banguela	Koch 2002	[108]
Homodiaetus passarellii	(Miranda Ribeiro 1944)	[29,70,108]
Callichthyidae
Callichthyinae
Callichthys callichthys	(Linnaeus 1758)	[1,14,15,16,25]
Hoplosternum littorale	(Hancock 1828)	[1,15,16,29]
Corydoradinae
Hoplisoma nattereri	(Steindachner 1876)	[1,18,29] as Corydoras nattereri
Scleromystax barbatus	(Quoy & Gaimard 1824)	[14,18,21,29]
Scleromystax prionotos	(Nijssen & Isbrücker 1980)	[70,109,110]
Loricariidae
Delturinae
Delturus parahybae	Eigenmann & Eigenmann 1889	[1,70]
Hemipsilichthys gobio	(Lütken 1874)	[4,19,29]
Hemipsilichthys nimius	Pereira, Reis, Souza & Lazzarotto 2003	[4,20,21]
Hemipsilichthys papillatus	Pereira, Oliveira & Oyakawa 2000	[4,17,19]
Rhinelepinae
Pogonopoma parahybae	(Steindachner 1877)	[27,70] as Pogonopomoides parahybae
Loricariinae
Harttia carvalhoi	Miranda Ribeiro 1939	[17,19]
Harttia loricariformis	Steindachner 1877	[17,19]
Loricariichthys castaneus	(Castelnau 1855)	[27,29,111]
Loricariichthys melanurus	Reis, Vieira & Pereira 2021	[111]
Rineloricaria nigricauda	(Regan 1904)	[70,112]
Rineloricaria nudipectoris	Mejia, Ferraro & Buckup 2023	[112]
Rineloricaria paraibensis	Mejia & Buckup 2024	[112]
Rineloricaria steindachneri	(Regan 1904)	[27,70,112]
Rineloricaria zawadzkii	Silva, Costa & Oliveira 2022	[112]
Hypoptopomatinae
Hisonotus notatus	Eigenmann & Eigenmann 1889	[29,113]
Hisonotus thayeri	Martins & Langeani 2016	[113]
Kronichthys heylandi	(Boulenger 1900)	[20,21,29,114]
Neoplecostomus microps	(Steindachner 1877)	[17,21,29,115]
Neoplecostomus paraty	Cherobim, Lazzarotto & Langeani 2017	[20,21,115]
Neoplecostomus variipictus	Bizerril 1995	[115]
Otocinclus affinis	Steindachner 1877	[1,70,110]
Otothyris lophophanes	(Eigenmann & Eigenmann 1889)	[1,70,110]
Pareiorhaphis garbei	(Ihering 1911)	[18,116]
Pareiorhina brachyrhyncha	Chamon, Aranda & Buckup 2005	[117]
Pareiorhina rudolphi	(Miranda Ribeiro 1911)	[19,20,21,117]
Parotocinclus bidentatus	Gauger & Buckup 2005	[5]
Parotocinclus fluminense	Roxo, Melo, Silva & Oliveira 2017	[118]
Parotocinclus maculicauda	(Steindachner 1877)	[1,18,29,114]
Parotocinclus muriaensis	Gauger & Buckup 2005	[5]
Pseudotothyris janeirensis	Britski & Garavello 1984	[1]
Schizolecis guentheri	(Miranda Ribeiro 1918)	[14,20,21,29,91]
Hypostominae
Ancistrus multispinis	(Regan 1912)	[18,20,21,29]
Hypostomus affinis	(Steindachner 1877)	[19,29,86]
Hypostomus auroguttatus	Kner 1854	[27,86]
Hypostomus luetkeni	(Steindachner 1877)	[1,16,19]
Hypostomus punctatus	Valenciennes 1840	[14,114]
Aspredinidae
Pseudobunocephalinae
Pseudobunocephalus iheringii	(Boulenger 1891)	[1] as Dysichthys iheringii
Auchenipteridae
Centromochlinae
Glanidium melanopterum	Miranda Ribeiro 1918	[1,110]
Auchenipterinae
Trachelyopterus striatulus	(Steindachner 1877)	[1] as Parauchenipterus striatulus, [16]
Heptapteridae
Rhamdiinae
Pimelodella lateristriga	(Lichtenstein 1823)	[14,19,20,21,29]
Rhamdia quelen	(Quoy & Gaimard 1824)	[14,21,29]
Heptapterinae
Acentronichthys leptos	Eigenmann & Eigenmann 1889	[16,18,20,21]
Imparfinis minutus	(Lütken 1874)	[17,19]
Imparfinis piperatus	Eigenmann & Norris 1900	[110,114]
Rhamdioglanis frenatus	Ihering 1907	[1,21]
Rhamdioglanis transfasciatus	Miranda Ribeiro 1908	[18,20,29]
Taunayia bifasciata	(Eigenmann & Norris 1900)	[21,119]
Pimelodidae
Pimelodus maculatus	Lacepède 1803	[27,86]
Steindachneridion parahybae	(Steindachner 1877)	[1]
Pseudopimelodidae
Batrochoglaninae
Microglanis nigripinnis	Bizerril & Perez-Neto 1992	[110,120]
Microglanis parahybae	(Steindachner 1880)	[110,120]
Microglanis pleriqueater	Mattos, Ottoni & Barbosa 2013	[120]
Clariidae
Clarias gariepinus *	(Burchell 1822)	[1]
Ariidae
Ariinae
Paragenidens grandoculis	(Steindachner 1877)	[121]
GYMNOTIFORMES
Sternopygidae
Eigenmanniinae
Eigenmannia virescens	(Valenciennes 1836)	[1,27,86,110]
Gymnotidae
Gymnotinae
Gymnotus carapo	Linnaeus 1758	[1,18,27,114]
Gymnotus pantherinus	(Steindachner 1908)	[1,18,19,20,29]
Hypopomidae
Brachyhypopomus janeiroensis	(Costa & Campos-da-Paz 1992)	[3,16,110]
SALMONIFORMES
Salmonidae
Salmoninae
Oncorhynchus mykiss *	(Walbaum 1792)	[1,21]
GOBIIFORMES
Eleotridae
Eleotrinae
Dormitator maculatus	(Bloch 1792)	[14,21]
Eleotris pisonis	(Gmelin 1789)	[14,16,20]
Oxudercidae
Gobionellinae
Awaous tajasica	(Lichtenstein 1822)	[14,16,20,25,29]
CICHLIFORMES
Cichlidae
Pseudocrenilabrinae
Coptodon rendalli *	(Boulenger 1897)	[1,14,16,27] as Tilapia rendalli
Oreochromis niloticus *	(Linnaeus 1758)	[1,14,19,27,29]
Cichlinae
Apistogramma sp. *		[1]
Astronotus ocellatus	(Agassiz 1831)	[1]
Australoheros ipatinguensis	Ottoni & Costa 2008	[122]
Australoheros oblongus	(Castelnau 1855)	[122]
Cichla kelberi *	Kullander & Ferreira 2006	[29,86,123]
Crenicichla lacustris	(Castelnau 1855)	[18,27,29,86]
Crenicichla sp.		MNRJ 48620
Geophagus brasiliensis	(Quoy & Gaimard 1824)	[1,14,16,21,25,29]
Parachromis managuensis *	(Günther 1867)	NPM 1796
ACANTHURIFORMES
Sciaenidae
Pachyurus adspersus	Steindachner 1879	[27,86,124]
CYPRINODONTIFORMES
Rivulidae
Rivulinae
Atlantirivulus guanabarensis	Costa 2014	[8]
Atlantirivulus janeiroensis	(Costa 1991)	[8], [14,25] as Rivulus janeiroensis
Atlantirivulus jurubatibensis	(Costa 2008)	[8,16]
Atlantirivulus lazzarotoi	(Costa 2007)	[8,21]
Atlantirivulus maricensis	Costa 2014	[8]
Atlantirivulus simplicis	(Costa 2004)	[8,21]
Kryptolebiatinae
Kryptolebias brasiliensis	(Valenciennes 1821)	[14,18,21,29]
Kryptolebias caudomarginatus	(Seegers 1984)	[14,125]
Kryptolebias gracilis	Costa 2007	[126]
Kryptolebias ocellatus	(Hensel 1868)	[14,21,125]
Cynolebiinae
Leptolebias marmoratus	(Ladiges 1934)	[127]
Leptopanchax citrinipinnis	(Costa, Lacerda & Tanizaki 1988)	[127,128]
Leptopanchax opalescens	(Myers 1942)	[127]
Leptopanchax sanguineus	Costa 2019	[127]
Leptopanchax splendens	(Myers 1942)	[127]
Nematolebias catimbau	Costa, Amorim & Aranha 2014	[129]
Nematolebias papilliferus	Costa 2002	[129]
Nematolebias whitei	(Myers 1942)	[129]
Notholebias cruzi	(Costa 1988)	[128]
Notholebias fractifasciatus	(Costa 1988)	[128]
Notholebias minimus	(Myers 1942)	[14] as Leptolebias minimus, [128]
Notholebias vermiculatus	Costa & Amorim 2013	[128]
Ophthalmolebias constanciae	(Myers 1942)	[130]
Poeciliidae
Poeciliinae
Phalloceros anisophallos	Lucinda 2008	[6,21]
Phalloceros enneaktinos	Lucinda 2008	[6,21]
Phalloceros harpagos	Lucinda 2008	[6,16,21,29]
Phalloceros leptokeras	Lucinda 2008	[6,21]
Phalloptychus januarius	(Hensel 1868)	[14,16,131]
Poecilia reticulata *	Peters 1859	[1,18,21,29]
Poecilia vivipara	Bloch & Schneider 1801	[1,18,21,29]
Xiphophorus helleri *	Heckel 1848	[1,18,29] as Xiphophorus sp.
Anablepidae
Anablepinae
Jenynsia darwini	Amorim 2018	[132]
Jenynsia lineata	(Jenyns 1842)	[14,16,131] as Jenynsia multidentata
SYNBRANCHIFORMES
Synbranchidae
Synbranchus sp.		[14,18,21,25,29,114] as S. marmoratus
ANABANTIFORMES
Osphronemidae
Trichogastrinae
Trichopodus trichopterus *	(Pallas 1770)	[1] as Trichogaster trichopterus
Macropodusinae
Trichopsis vittata *	(Cuvier 1831)	MNRJ 40423

), distributed across 10 orders, 33 families, 42 subfamilies, and 102 genera.

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

Lots sampled. A total of 19,770 lots from the collections were recorded. Among these, 4466 (22.59%) were from the oceanic area. Additionally, 379 fish records (1.92%) were not identified at the species level. The 4466 marine lots, 1340 estuarine lots, and 379 lots of unidentified material were not evaluated in the present study but are available in the Supplementary Material. The remaining fish lots were considered for analysis (

Table 1. Lots sampled in the state of Rio de Janeiro.

	Ocean	Continental	Not Identified	Total	%
Marine origin	4466	1340	-----	5806	29.37%
Allochthonous freshwater	-----	533	-----	533	2.70%
Native freshwater	-----	13.052	-----	13.052	66.02%
Not identified	-----	-----	379		1.92%
Total	4466	14,925	379	19.770
%	22.59%	75.49%	1.92%

).

Of the 13,585 lots sampled from continental waters of the state of Rio de Janeiro, 533 (2.7%) are from allochthonous species and 13,052 (66.02%) are from native species. The available species names were updated to include only valid names. A detailed revisionary study of fish species in the territory is beyond the scope of this research.

Regarding the geographical accuracy of the 13,585 freshwater records, 4012 had their coordinates provided correctly, 5527 coordinates were adjusted, and 4046 coordinates were estimated based on locality data. This estimation considered factors, such as historical pathways, available roads, and access to sampling sites, particularly for older records deposited in collections before the advent of georeferencing systems.

Sampling index. The average sampling index for deposited lots of freshwater fish in the state of Rio de Janeiro was 31.0 per 100 km². The average index of sampling points on the continent was 4.2 per 100 km²

Three hydrographic regions were adequately sampled, considering both indices (

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	1919.1	3270	146	170.4	Good	7.6	Good
RH-2	Guandu	4087.8	2069	256	50.6	Good	6.3	Good
RH-3	Middle Paraíba do Sul	7114.1	1822	149	25.6	Average	2.1	Poor
RH-4	Piabanha	3831.4	1400	123	36.5	Good	3.2	Average
RH-5	Baía de Guanabara	696.0	1342	479	192.8	Good	68.8	Good
RH-6	Lagos São João	4030.2	1125	178	27.9	Average	4.4	Average
RH-7	Rio Dois Rios	4940.9	987	89	20.0	Poor	1.8	Poor
RH-8	Macaé and das Ostras	2226.9	823	166	37.0	Average	7.5	Good
RH-9	Lower Paraíba do Sul and Itabapoana	14,904.0	746	267	5.0	Poor	1.8	Poor
	Rio de Janeiro State	43.750	1.585	1854	31.1		4.2

). In contrast, two regions exhibit poor sampling quality. Four other regions have sampling quality that ranges from good to poor, depending on the index considered. Overall, the regions with lower sampling quality mainly correspond to the Paraíba do Sul valley, especially in its lower portion. See discussion for more detail.

Among the 92 municipalities in Rio, 27 have a good number of lots (

Table A1. Quality of sampling per municipalities in the state of Rio de Janeiro.

Municipality	Area (km2)	Lots	Points	Il	Ilq	Ip	Ipq
Angra dos Reis	813.21	532	57	65.4	Good	7.0	Good
Aperibé	94.54	2	2	2.1	Poor	2.1	Poor
Araruama	638.15	23	4	3.6	Poor	0.6	Poor
Areal	110.72	0	0	0.0	Poor	0.0	Poor
Armação dos Búzios	70.98	2	1	2.8	Poor	1.4	Poor
Arraial do Cabo	152.11	0	0	0.0	Poor	0.0	Poor
Barra do Piraí	584.61	53	11	9.1	Poor	1.9	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	70	20	16.9	Poor	4.8	Average
Cachoeiras de Macacu	954.75	944	93	98.9	Good	9.7	Good
Cambuci	558.28	6	2	1.1	Poor	0.4	Poor
Campos dos Goytacazes	4032.49	761	93	18.9	Poor	2.3	Poor
Cantagalo	747.21	56	7	7.5	Poor	0.9	Poor
Carapebus	304.89	261	25	85.6	Good	8.2	Good
Cardoso Moreira	522.6	24	3	4.6	Poor	0.6	Poor
Carmo	305.75	256	24	83.7	Good	7.8	Good
Casimiro de Abreu	462.92	355	44	76.7	Good	9.5	Good
Comendador Levy Gasparian	108.64	1	1	0.9	Poor	0.9	Poor
Conceição de Macabu	338.26	114	16	33.7	Average	4.7	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	269	42	57.6	Good	9.0	Good
Engenheiro Paulo de Frontin	139.38	30	4	21.5	Poor	2.9	Poor
Guapimirim	358.44	294	30	82.0	Good	8.4	Good
Iguaba Grande	50.98	6	2	11.8	Poor	3.9	Average
Itaboraí	429.96	132	11	30.7	Average	2.6	Poor
Itaguaí	282.61	150	23	53.1	Good	8.1	Good
Italva	291.19	17	2	5.8	Poor	0.7	Poor
Itaocara	433.18	120	9	27.7	Average	2.1	Poor
Itaperuna	1106.69	313	17	28.3	Average	1.5	Poor
Itatiaia	241.04	231	41	95.8	Good	17.0	Good
Japeri	81.7	59	3	72.2	Good	3.7	Average
Laje do Muriaé	253.53	7	1	2.8	Poor	0.4	Poor
Macaé	1216.99	827	102	68.0	Good	8.4	Good
Macuco	78.36	0	0	0.0	Poor	0.0	Poor
Magé	390.78	335	46	85.7	Good	11.8	Good
Mangaratiba	367.82	61	11	16.6	Poor	3.0	Average
Maricá	361.57	233	63	64.4	Good	17.4	Good
Mendes	95.32	46	3	48.3	Good	3.1	Average
Mesquita	41.17	37	4	89.9	Good	9.7	Good
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	8	2	2.1	Poor	0.5	Poor
Nilópolis	19.39	0	0	0.0	Poor	0.0	Good
Niterói	133.76	22	10	16.4	Average	7.5	Good
Nova Friburgo	935.43	190	35	20.3	Poor	3.7	Average
Nova Iguaçu	520.58	488	64	93.7	Good	12.3	Good
Paracambi	190.95	24	3	12.6	Poor	1.6	Poor
Paraíba do Sul	571.12	47	6	8.2	Poor	1.1	Poor
Paraty	924.3	592	88	64.0	Good	9.5	Good
Paty do Alferes	314.34	8	2	2.5	Poor	0.6	Poor
Petrópolis	791.14	146	26	18.5	Poor	3.3	Average
Pinheiral	82.25	2	1	2.4	Poor	1.2	Poor
Piraí	490.26	115	17	23.5	Average	3.5	Average
Porciúncula	291.85	0	0	0.0	Poor	0.0	Poor
Porto Real	50.89	51	5	100.2	Good	9.8	Good
Quatis	284.83	51	8	17.9	Poor	2.8	Poor
Queimados	75.93	33	2	43.5	Good	2.6	Poor
Quissamã	719.64	339	56	47.1	Good	7.8	Good
Resende	1099.34	290	47	26.4	Average	4.3	Average
Rio Bonito	459.46	32	5	7.0	Poor	1.1	Poor
Rio Claro	846.8	721	75	85.1	Good	8.9	Good
Rio das Flores	478.78	10	2	2.1	Poor	0.4	Poor
Rio das Ostras	228.04	40	8	17.5	Poor	3.5	Average
Rio de Janeiro	1200.33	904	194	75.3	Good	16.2	Good
Santa Maria Madalena	810.96	84	20	10.4	Poor	2.5	Poor
Santo Antônio de Pádua	603.63	44	3	7.3	Poor	0.5	Poor
São Fidélis	1034.83	257	16	24.8	Average	1.5	Poor
São Francisco de Itabapoana	1118.04	111	19	9.9	Poor	1.7	Poor
São Gonçalo	248.16	3	2	1.2	Poor	0.8	Poor
São João da Barra	452.4	239	26	52.8	Good	5.7	Good
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	28	6	8.4	Poor	1.8	Poor
São Sebastião do Alto	397.21	107	11	26.9	Average	2.8	Poor
Sapucaia	540.67	95	14	17.6	Poor	2.6	Poor
Saquarema	352.13	220	45	62.5	Good	12.8	Good
Seropédica	265.19	135	29	50.9	Good	10.9	Good
Silva Jardim	937.76	692	57	73.8	Good	6.1	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	228	42	29.5	Average	5.4	Average
Trajano de Moraes	591.15	30	6	5.1	Poor	1.0	Poor
Três Rios	322.84	196	19	60.7	Good	5.9	Good
Valença	1300.77	8	5	0.6	Poor	0.1	Poor
Varre-Sai	201.94	0	0	0.0	Poor	0.0	Poor
Vassouras	536.07	87	6	16.2	Poor	1.1	Poor
Volta Redonda	182.11	14	4	7.7	Poor	2.2	Poor
Rio de Janeiro State	43,750	13,585	1854	31.1		4.2

), 11 are within the state average, and 54 have a poor number of lots. Regarding sampling points, 27 municipalities have a good number of points, 12 are within the state average, and 53 have a poor number of sites (Figure 3B). Nine municipalities (Areal, Arraial do Cabo, Belford Roxo, Macuco, Miracema, Nilópolis, Porciúncula, São João de Meriti, and Varre-Sai) do not have any records of freshwater species in the ichthyological collections. Arraial do Cabo only has records of marine species.

Eleven basin groups have a good number of deposited lots, two are within the state average, and another eleven ones show poor quality in this index (

Table 3. Sample quality per basin group.

Code	Basin Groups	Area (km2)	Lots	Points	Il	Ilq	Ip	Ipq
08.20	Itabapoana	1523.4	129	20	8.5	Poor	1.2	Poor
08.21	São Francisco do Itabapoana	971.3	32	6	3.3	Poor	0.6	Poor
08.22a	Rio Paraíba do Sul—RH-9	6321.4	991	81	15.7	Poor	1.3	Poor
08.22b	Rio Paraíba do Sul—RH-7	4468.6	746	88	16.7	Poor	12.0	Poor
08.22c	Rio Paraíba do Sul—RH-4	3469.0	823	122	23.7	Average	3.5	Average
08.22d	Rio Paraíba do Sul—RH-3	6430.8	989	149	15.3	Poor	2.3	Poor
08.22e	Rio Paraíba do Sul—RH-2	1014.1	652	73	64.3	Good	7.2	Good
08.23	Lagoa Feia	4310.8	651	122	15.1	Poor	2.8	Poor
08.24	Jurubatiba	410.1	695	79	169.5	Good	19.3	Good
08.25	Rio Macaé	1706.4	924	117	54.1	Good	6.9	Good
08.26	Rio das Ostras	249.4	46	10	18.4	Poor	4.0	Average
08.27	Rio São João	2155.6	1010	101	46.9	Good	4.7	Average
08.28	Rio Una and Búzios	541.7	15	6	2.8	Poor	1.1	Poor
08.29	Lagoa de Araruama	677.5	46	16	6.8	Poor	2.4	Poor
08.30	Saquarema	265.2	247	53	93.1	Good	20.0	Good
08.31	Maricá	349.5	206	55	58.9	Good	15.7	Good
08.32	Niterói	51.5	10	7	19.4	Poor	13.6	Good
08.33	Baía de Guanabara	4073.8	2519	297	61.9	Good	7.3	Good
08.34	Rio de Janeiro	2636.3	1622	283	61.5	Good	10.7	Good
08.35	Sepetiba	107.0	46	13	43.0	Average	12.1	Good
08.36	Mangaratiba	289.8	61	11	21.0	Poor	3.8	Average
08.37	Angra dos Reis	1028.2	548	62	53.3	Good	6.0	Good
08.38	Paraty	376.9	299	49	79.3	Good	13.0	Good
08.39	Cairuçu	322.2	278	34	86.3	Good	10.6	Good
	Rio de Janeiro State	43.750	13.585	1854	31.1		4.2

). Twelve basins have a good number of sampling points, four are within the state average, and eight have poor coverage in this aspect.

Temporal variation. The earliest records deposited in ichthyological collections in this assessment date back to 1832. From 1980 onwards, the collections experienced significantly higher growth compared to the entire preceding period, with continuous increases since 2000, especially in the 2010s (Figure 4). The historical reasons behind the rise in deposition numbers within the territory are beyond the scope of this contribution.

Taxonomic Diversity

Native species. Siluriformes (94 spp., 51.4%), Characiformes (43 spp. 23.5%), and Cyprinodontiformes (31 spp., 16.9%) are the most species-rich orders among the native species recorded in Rio de Janeiro (Figure 5A). The most representative families of the total (Figure 5B) are Loricariidae (36 spp., 19.7%), Trichomycteridae (36 spp., 19.7%), Characidae (24 spp., 13.1%) and Rivulidae (23 spp., 12.6%). Trichomycterus (24 species), Characidium, and Deuterodon (7 species) are the richest genera. A special mention is given to two species in the metropolitan area of Rio de Janeiro that were not included in our assessment. Astyanax scabripinnis (Jenyns 1842) is a species restricted to the state of Rio de Janeiro. It was first described based on a single specimen collected by Charles Darwin during his stay in the city of Rio de Janeiro from April to July 1832, according to Jenyns (1842) (holotype BMNH 1917.7.14.15, Rio de Janeiro). The region explored by Darwin included the small drainages flowing into Guanabara Bay. Two centuries later, ref. [63] revised the Astyanax species from the Serra dos Órgãos, a region of rivers draining towards Guanabara Bay, but did not find any populations that could be clearly identified as A. scabripinnis, raising the possibility of species extinction. Although Astyanax scabripinnis is currently considered part of a species complex, the nominal species Astyanax scabripinnis is possibly extinct [63,64]. Another peculiar situation involves Harttia rhombocephala, known from a single specimen collected in the Rio Farias, a small creek that is a tributary of Guanabara Bay (holotype MNRJ 712). This river is a tributary of the Rio Jacaré, within the Guanabara Bay hydrographic region [65]. Currently, these rivers suffer from severe anthropogenic impacts, as they are located in the densely populated metropolitan area of Rio de Janeiro.

Allochthonous species. Characiformes and Cichliformes account for more than 70% of non-native species resulting from intentional or accidental introductions due to aquarism, biological control, and fish farming. Poecilia reticulata, native to northern South America and the Caribbean, is the most abundant non-native species in the rivers of the state, widely distributed across various basins, followed by Coptodon rendalli, originating from Southern–Central Africa, and Hyphessobrycon eques, native to the Amazon Basin and the Rio Paraguay [35]. Rio de Janeiro has a long history in the aquarium trade in Brazil [66]. Since the 1940s, aquarium keepers have engaged in commerce with European markets. Some of these freshwater aquarium fish have been deposited in ichthyological collections [67]. The early aquarist teams contributed significantly to the knowledge of natural biodiversity [66] and left behind specimens of other common aquarium fish in museum collections, most of which are alien to the native fauna of Rio de Janeiro. More recently, collections have received records of fish introduced for commercial purposes and those documented during environmental impact assessments conducted by various enterprises.

Constancy index. A total of 1854 unique sampling points were identified in the state of Rio de Janeiro. No species was classified as constant, and only Geophagus brasiliensis appeared as an accessory species, being recorded in 555 points (29.9%). The others were all accidental, such as Poecilia vivipara in 319 sites (17.2%) and Phalloceros harpagos in 208 sites (11.2%).

Non-parametric estimation and diversity indices. The largest variations in species richness indicated by the non-parametric estimators (

Table 4. 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	46	110	82	81	109	92	80	82	99
Individuals	20,687	27,497	5435	10,689	37,243	13,048	4462	31,063	43,833
Dominance_D	0.10	0.08	0.04	0.09	0.05	0.06	0.04	0.12	0.14
Shannon_H	2.76	3.27	3.53	2.94	3.57	3.53	3.60	2.62	2.68
Equitability_J	0.72	0.70	0.80	0.67	0.76	0.78	0.82	0.59	0.58
Chao-1	46.3	111.2	93.0	86.1	109.3	92.8	85.0	86.2	105.0
iChao-1	46.7	112.3	98.0	89.4	110.2	93.5	90.0	89.4	108.7
ACE	48.3	111.6	91.1	87.5	109.6	93.8	83.1	86.3	104.7
Squares	46.4	111.3	87.4	88.4	109.2	92.5	81.5	87.8	110.0
Variation in species richness for the non-parametric estimation indices
Chao-1	0.5%	1.1%	13.4%	6.3%	0.3%	0.8%	6.3%	5.1%	6.1%
iChao-1	1.5%	2.1%	19.5%	10.3%	1.1%	1.6%	12.5%	9.0%	9.8%
ACE	4.9%	1.5%	11.0%	8.0%	0.6%	1.9%	3.9%	5.3%	5.8%
Squares	0.9%	1.2%	6.5%	9.2%	0.2%	0.5%	1.8%	7.0%	11.1%

) are related to the Médio Paraíba do Sul hydrographic region (RH-3) (ranging from 6.5% to 19.5%), followed by the Rio Dois Rios hydrographic region (RH-7) (1.8% to 12.5%) and the Lower Paraíba do Sul and Itabapoana hydrographic region (RH-9) (5.8% to 11.1%). All regions with higher variations in species richness estimators are associated with the Paraíba do Sul River basin.

The Guandu hydrographic region (RH-2) exhibited the highest species richness within the state, with 110 species, closely followed by the Guanabara Bay hydrographic region (RH-5), with 109 species. The lowest richness was observed in the Baía da Ilha Grande hydrographic region (RH-1), with 46 species. The RH-3 and Macaé and das Ostras hydrographic region (RH-8) each had 82 species, the Piabanha hydrographic region (RH-4) had 81 species, and RH-7 had 80 species, all showing similar levels of richness. The Shannon index indicates the greatest diversity in RH-7 (H = 3.60), followed by RH-5 (H = 3.57). The lowest diversity was recorded in RH-8 (H = 2.62).

The greatest species richness in RH-2 (110 spp.) is linked to the fact that this hydrographic region, encompassing coastal rivers, is connected to the Rio Paraíba do Sul through a river transfer system with the Rio Guandu basin, which supplies water to the city of Rio de Janeiro. In contrast, the lowest species richness in RH-1 (46 spp.) is related to the geographic characteristics of this region, where the Serra do Mar is very close to the coast. The habitats in RH-1 are characterized either by steep rapids or estuarine plains.

The beta-diversity NMDS plot (stress = 0.078, Figure 6) was able to consistently discriminate three clusters of hydrographic regions in the state: Lower Paraíba do Sul basin (RH-4, 7, and 9), coastal drainages in the oceanic slope of Serra do Mar (RH-2, 5), and basins of the Lagos region (RH-6 and 8). The Ilha Grande (RH-1) and the Middle Paraíba do Sul (RH-3) regions stood out as outliers.

Although Rio de Janeiro is well-sampled within the Atlantic Forest, the sampling is not uniform. Quality indices for the sampled lots are good for areas ranging between 20% and 32% of the state (depending on the division by municipalities, basins, or hydrographic regions). In contrast, between 11% and 18% of the state area has average sampling quality, while between 55% and 60% of the state presents poor sampling quality.

3.2. Spatial Patterns of Distribution

Seven bioregions have been delineated based on patterns of distribution of the freshwater fish fauna (Figure 7A). The delineated bioregions bear similarities to the configuration generated by the beta-diversity analysis. These areas are herein referred to as Lower Rio Paraíba do Sul bioregion, Middle Rio Paraíba do Sul I and II bioregions, Lagos-São João bioregion, Lagos-Saquarema bioregion, Guanabara-Sepetiba bioregion, and Costa Verde bioregion.

• Lower Rio Paraíba do Sul bioregion: The largest area in terms of territorial extension, this biogeographic unit consists of Rio Piabanha, Rio Dois Rios, lower Rio Paraíba do Sul basin and its tributaries, such as the Rio Muriaé, Rio Dois Rios and Rio Pomba, as well as independent coastal basins, such as the Rio Itabapoana (the geographical divide with the state of Espírito Santo), Rio Macaé, and coastal lagoon systems. Taxa that delimit this bioregion are shared with other basins in southern Espírito Santo, such as Rineloricaria steindachneri, Hypomasticus thayeri, and Loricariichthys melanurus, as well as Scleromystax prionotos, Trichomycterus puriventris and T. fuliginosus. It is the most speciose bioregion, presenting 120 species, of which 23 occur only in this area (e.g., Astyanax jenynsii, A. jurubatibensis, Delturus parahybae, Homodiaetus banguela, Ituglanis parahybae, Listrura macaensis, Parotocinclus muriaensis, Scleromystax prionotos, Trichomycterus caipora, T. fuliginosus, T. paquequerense, and T. vitalbrazili). Covering a large part of the basins of the state of Rio de Janeiro, many of the species present in these drainages are common to several other drainages of the Atlantic Forest, such as fish species within the genera Deuterodon, Hypostomus, and Trichomycterus.
• Middle Paraíba do Sul I bioregion: The bioregion consists mainly of a middle section of the Rio Paraíba do Sul basin and its tributaries, such as the Rio das Flores and Rio Preto, under the influence of the Serra da Mantiqueira. This area has a particularly high diversity of species of the Trichomycterus genus, including T. albinotatus, T. mirissumba, T. quintus, and T. itatiayae, which support the delimitation of this region. Fish occurring in this bioregion are commonly present in other reaches of the upper and middle Rio Paraíba do Sul in the states of São Paulo and Minas Gerais.
• Middle Rio Paraíba do Sul II bioregion: This area of the middle Rio Paraíba do Sul consists primarily of the Rio Piraí basin. This region also presents considerable diversity of species of the Trichomycterus genus, such as T. claudiae, T. macrophtalmus, and T. nigroauratus, which outline the area.
• Lagos-São João bioregion: The Rio São João basin and the coastal systems of Búzios, Una and Araruama Lagoon constitute this region, delimited by species, such as Nematolebias whitei, Listrura menezesi, Listrura tetraradiata, Parotocinclus fluminense, and Homodiaetus banguela.
• Lagos-Saquarema bioregion: This bioregion consists of the coastal basins of Maricá and Saquarema and adjacent lagoon systems. This delineation is mainly supported by rivulid species, such as Atlantirivulus maricensis, Leptopanchax citrinipinnis, Nematolebias catimbau, Nematolebias papilliferus, Notholebias fractifasciatus, and Notholebias vermiculatus, all of which are endemic to this area.
• Guanabara-Sepetiba bioregion: This bioregion consists of rivers that mostly originate on the oceanic slopes of the Serra do Mar and flow into the Guanabara and Sepetiba bays, as well as coastal lagoon systems in the lowland areas, such as Jacarepaguá. This unit includes relevant basins for the metropolitan region of the state, such as the Rio Caceribu, Rio Guapimirim, Rio Macacu, Rio Roncador, Rio Suruí, Rio Iguaçu, and Rio Guandu. The second most diverse bioregion in the state, it presents 105 species, 17 of which are endemic, such as several rivulids and trichomicterids that support this delimitation (e.g., Atlantirivulus guanabarensis, Kryptolebias caudomarginatus, Leptolebias marmoratus, Leptopanchax opalescens, Leptopanchax sanguineus, Leptopanchax splendens, Notholebias minimus, Homodiaetus passarellii, and Listrura nematopteryx).
• Costa Verde bioregion: This biogeographic unit comprises small basins that flow into Ilha Grande Bay, including the drainages of the Rio Mambucaba, Rio Perequê-Açu, Rio Taquari, and Rio Parati-Mirim, among others. This region is supported by the presence of endemic species, such as Atlantirivulus lazzarotoi, A. simplicis, Listrura costai, Phalloceros enneaktinos, Characidium japuhybense, Hemipsilichthys nimius, and Neoplecostomus paraty.

The cluster analysis (cophenetic correlation = 0.9) indicates that the Lower Paraíba do Sul and Guanabara-Sepetiba bioregions exhibit approximately 47% ichthyofaunistic similarity (Figure 7B). This grouping shows approximately 43% similarity with the Lagos-São João bioregion. Consistent with the beta-diversity analysis, the Costa Verde and Middle Paraíba I and II regions diverge from the other basins in the state, showing less than 20% ichthyofaunistic similarity.

The species richness interpolation results suggests that the greatest diversity in the state is located in a large area corresponding to the drainages around Guanabara Bay, extending to the east and west and encompassing various basins originating from the Serra do Mar (e.g., Rio Macacu, Rio Suruí, Rio Guapimirim), as well as on the opposite slope of this mountain range, covering tributaries of the Rio Paraíba do Sul, such as the headwaters of the Rio Piabanha and Rio Dois Rios (Figure 8).

The weight endemism index was higher in several areas of the state (Figure 9): in the headwaters to the east of Guanabara Bay, such as the Rio Macacu and Rio Caceribu; in the systems to the north and west of Guanabara Bay, such as the Rio Iguaçu, Rio Estrela, Rio Suruí, and Rio Roncador; in the headwaters of Rio São João; in coastal areas of Maricá and Saquarema; in the headwaters of the Rio Piraí; in the reach of the middle Rio Paraíba do Sul in the Serra da Mantiqueira; and in the basins of the Costa Verde region. Areas of low endemism mainly include stretches of the middle and lower Rio Paraíba do Sul and other lowland areas in the east and northeast of the state.

4. Discussion

4.1. Taxonomic Diversity and Sampling Coverage

The orders Siluriformes and Characiformes were predominant in terms of the number of species, repeating a pattern commonly found in drainages of the Neotropical region and the Atlantic Forest biome [68,69], 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 Atlantic Forest. This result is in line with several regional studies for the Rio de Janeiro basins (e.g., [15,16,17,18,19,20,21]). There has been a substantial increase in the number of species described and reported for Rio de Janeiro since the last comprehensive assessment [1], from 149 freshwater species to 205 (an increase of nearly 40%), including non-native and exotic species. Closing these taxonomic and distributional gaps is largely attributed to intensive research on taxa with complex or unclear taxonomy (e.g., species complexes) and the exploration of undersampled areas, which has led to the discovery of new fish species in the state.

However, there are still many areas with low sampling coverage in Rio de Janeiro, particularly in the Rio Paraíba do Sul basin. The results indicate that most municipalities and hydrographic regions within the basin have an average to poor representativity of sampling points and specimens deposited in collections. These areas, especially in the lower section, are traditional areas of coffee cultivation and are now largely deforested, with river siltation and pollution. In contrast, the more sampled and diversified freshwater area—the vicinities of the Guanabara Bay—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 has historically facilitated access. While the state has good overall sampling coverage, further targeted investigations are still necessary in certain regions.

4.2. Biogeographic Patterns

The perception that the rivers of the Atlantic Forest in Rio de Janeiro comprise sets of heterogenous, distinct areas of endemism for fish has been previously detailed by authors who have analyzed the biome as a whole [68,70]. Subsequently, ref. [71], in a global analysis of freshwater ecoregions, recognized 3 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 [71]). The division of Rio de Janeiro into bioregions of ichthyofaunal endemism recognized here partially matches the findings of [71]. The bioregions Lower Rio Paraíba do Sul and Middle Rio Paraíba do Sul I and II loosely adjust to ecoregion 329. The bioregion Costa Verde corresponds to part of the Ribeira do Iguape ecoregion. Additionally, the bioregions Guanabara-Sepetiba, Lagos-Saquarema, and Lagos-São-João almost corroborate the delineation of the Fluminense ecoregion, as they are also in a similar configuration to the results of the beta-diversity analysis.

However, it is possible that the delimitation of ecoregions proposed by [71] may not fully align with faunal distributions when considering (a) the geomorphological histories of the basins, reflected in the faunal particularities of each area, and (b) the total species composition of the regions, as opposed to the three groups chosen by [71] to define the eastern coastal ecoregions (the extinct subfamily Neoplecostominae and the families Trichomycteridae and Rivulidae) [72,73,74,75]. Nonetheless, dividing areas into biogeographic units at different levels of detail is valuable not only for the biogeographic and evolutionary insights they provide but also because they offer a more focused framework for applying conservation efforts.

The bioregions delineated, as well as the species turnovers, appear to be influenced and partially divided by the Serra do Mar, despite plausible hypotheses of past headwater captures between adjacent basins on opposite slopes. Its continental slope is dominated by the many tributaries of the Rio Paraíba do Sul basin. The hydrographic region of the Middle Paraíba do Sul (and the bioregions Middle Paraíba do Sul I and II) stands out from other middle-lower sections of the basin due to its greater faunal similarity with the upper Paraíba do Sul, located in the state of São Paulo [27,33]. On the oceanic slope of the Serra do Mar, short and independent coastal rivers, mostly originating from its escarpments, drain the entire territory up to the lower Paraíba do Sul region. This area corresponds to RH-1, 2, 5, 6, and 8 and the bioregions Costa Verde, Guanabara-Sepetiba, Lagos-Saquarema, and Lagos-São João, which share more similarities with each other than with the basins of the Middle Rio Paraíba do Sul.

Interestingly, there is nearly 50% faunal similarity between the Guanabara-Sepetiba and Lower Paraíba do Sul bioregions according to the cluster analysis (the lower Paraíba do Sul region is located on both slopes of the Serra do Mar). These areas share several species common to various rivers in the state, such as Cyphocharax gilbert, Deuterodon hastatus, Hyphessobrycon reticulatus, Hypostomus punctatus, Loricariichthys castaneus, Mimagoniates microlepis, Parotocinclus maculicauda, and Prochilodus lineatus, among others. However, congruent species distributions do not necessarily indicate a single biogeographic history between areas [75]. Despite the similarities, these bioregions also present unique species assemblages, which may explain their segregation by this method.

It is speculated that the uplift of the Serra do Mar and Serra da Mantiqueira during the Early Miocene (23–16 Ma) altered the flow of the Upper Paraná River basin, isolating tributaries that then began to run into the Atlantic Ocean, potentially leading to various subsequent vicariance and geodispersal events [76,77]. This later diversification, accompanied by numerous geographic isolation events, possibly driven by tectonic reactivations and erosive processes on the Serra do Mar escarpments, may be responsible for the icthyofaunistic differences found on both sides of this mountain range. However, as previously mentioned, some hypotheses suggest possible later headwater capture events that dispersed some species between adjacent basins on both slopes, such as Phalloceros leptokeras, which geodispersed from the Rio Macacu basin to the Rio Piabanha, a tributary of the Rio Paraíba do Sul [78], and Astyanax viridis, also found in the Rio Macacu basin, Rio Piabanha, and other drainages in the state [79].

These geological developments also help to explain patterns of endemism in highly restricted species found in basins draining the Serra do Mar. In particular, there is significant endemism among rivulid and catfish species in areas delineated by the weight endemism analysis, such as the headwaters of the Rio Macacu and Rio Caceribu (Listrura macacuensis, Microcambeva bendego, and Microglanis nigripinnis); Rio São João (Homodiaetus banguela, Listrura menezesi, and Parotocinclus fluminense); in the systems to the north and west of Guanabara Bay, such as the Rio Iguaçu, Rio Estrela, Rio Suruí, and Rio Roncador (Leptolebias marmoratus, Leptopanchax opalescens, Leptopanchax sanguineus, Leptopanchax splendens, and Listrura nematopteryx); in the coastal areas of Maricá and Saquarema (Atlantirivulus maricensis, Leptopanchax citrinipinnis, Listrura tetraradiata, Nematolebias catimbau, Nematolebias papilliferus, Notholebias fractifasciatus, Notholebias vermiculatus, and Trichomycterus saquarema); in the headwaters of the Rio Piraí (Trichomycterus macrophthalmus and T. nigroauratus); in the reach of the Middle Rio Paraíba do Sul in the Serra da Mantiqueira (Pareiorhina brachyrhyncha, Trichomycterus albinotatus, T. auroguttatus, T. itatiayae, T. mirissumba, and T. quintus, also found in sections of the Paraíba do Sul basin in other states); and in the basins of the Costa Verde region, discussed in detail further below. Apart from the Middle Paraíba do Sul section, these areas represent distributions of endemic species in Rio de Janeiro with a narrow geographic range (in contrast to state-endemic species distributed across multiple basins, such as Astyanax viridis, Atlantirivulus guanabarensis, and Characidium grajahuense, among others).

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 pronounced endemism of species, with a unique set of stream fishes, such as Atlantirivulus lazzarotoi, A. simplicis, Characidium japuhybense, Hemipsilichthys nimius, Listrura costai, Neoplecostomus paraty, and Phalloceros enneaktinos [21]. Possible reasons for this include the presence of numerous coastal drainages flowing from the Serra do Mar, with some of them presenting coastal floodplain areas with bays and lagoon systems. These diverse environments support a significant diversity of small-sized fish species, with a standard length of 15 cm or less. Although these small-sized species represent about 70% of the total diversity in the Neotropical region, their existence is often almost neglected [80]. Being mostly fish of the primary division, 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. This topographic configuration has significant implications for aquatic biota, as isolated basins often present similar fish fauna [76]. The coastal basins of the Atlantic Forest, where the Serra do Mar is very close to the coast, such as in the Costa Verde area, were influenced by climate change that caused sea level oscillations during the Pleistocene. These transgressions and regressions led to the isolation and reconnection of rivers [81]. Past connections among these coastal basins are hypothesized by [82] based on molecular evidence and paleodrainage reconstruction.

The particularities of the relief and the configuration of the hydrographic basins in the Atlantic Forest biome within Rio de Janeiro include the Paraíba valley—depressed tectonic valley corridors along faults [83]—with rivers flowing between the 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 identified as the most diverse area for freshwater fishes in its entirety. However, its diversity remains significant, considering that the basin is located among the largest urban–industrial centers of the state. Nevertheless, for this reason, the ichthyofauna faces various threats. Major impacts include environmental degradation, dam construction, the destruction of riparian forests, untreated discharge of domestic and industrial sewage, and mining. On the other hand, approximately ten endangered freshwater fish species inhabit the Rio Paraíba do Sul Valley and are the focus of conservation efforts [84]. The mountainous terrain of this region, characterized by vertical escarpments and difficult access, which helped keep the mountain barrier impassable during the early centuries of colonization [85], has acted as a geographical divide, isolating the Rio Paraíba do Sul from the coastal rivers.

5. Conclusions

This is the first comprehensive evaluation of freshwater fishes in the Rio de Janeiro region with precise geographic data. We hope that our findings will advance future research and inform conservation management strategies for this complex and diverse area, serving as a foundational step toward further assessments. The distribution patterns of fish species in the region support earlier studies of the Atlantic Forest and suggest that the identified bioregions align partially with previously established biogeographic units.