The use of fish diversity and abundance as environmental indicators in a mining region in Brazilian Amazonia

The use of fish diversity and abundance as environmental indicators in a mining region in Brazilian Amazonia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The use of fish diversity and abundance as environmental indicators in a mining region in Brazilian Amazonia Claudia Antônia Campos Rodrigues de Oliveira, Katherine Soares Morais, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7887845/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The impacts of mining on aquatic biodiversity pose significant challenges for environmental mitigation, particularly near protected areas. This study assessed fish abundance and diversity (assemblage composition, alpha and beta diversity, indicator species, and Species Contribution to Beta Diversity – SCBD) in the Itacaiúnas and Parauapebas river basins, Brazilian Amazonia, across dry and rainy seasons. We hypothesized that basins near mining-impacted areas would exhibit reduced fish abundance and diversity compared to control areas. To test this, four sampling points were established per basin (two controls, two impacted), totaling eight sites, where fish were collected using gillnets and physicochemical water parameters were simultaneously recorded. Fish abundance and alpha diversity peaked during the rainy season but showed no significant differences between control and impacted areas. However, beta diversity was highest in the Parauapebas basin, and distinct indicator species emerged: Plagioscion squamosissimus and Pygocentrus nattereri were associated with control areas, while Satanoperca jurupari characterized impacted sites. A Redundancy Analysis (RDA) linked environmental variables (temperature, salinity, conductivity) to species distributions, reflecting water quality influence. Our data suggest that phenotypic plasticity might mask mining's negative effects by favoring generalist species. While abundance and richness did not clearly differentiate areas, beta diversity patterns and indicator species highlight the critical need for continuous, integrated monitoring to assess long-term ecological shifts. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Fish assemblage Impacts of mining Amazon Basin Biodiversity indicators Alpha and beta diversity Phenotypic plasticity Environmental monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The Amazon basin is widely considered to be a hotspot of fish diversity, given that it is the world’s most diverse biome and has the richest freshwater ichthyofauna. However, the Amazon basin has been facing a series of anthropogenic pressures that threaten its future and ecological stability, and have drastically reduced its biodiversity and driven many fish species toward extinction 1 , 2 , 3 , 4 . The human activities that threaten biodiversity are considered to be critical environmental issues, given that conservation is not only essential for saving species, but also contributes to the preservation of habitats. In the Amazon basin, the consequences of urban growth, unfettered deforestation, the degradation of natural resources, the lack of basic sanitation, squatting, and mining, have caused an enormous range of impacts that affect the aquatic biota directly 5 , 6 , 7 . In the Amazon region, mining has put even more pressure on areas that are crucial for biodiversity conservation 8 , 9 such as the Carajás Mineral Province (CMP), which is the largest reserve of mineral resources in the Amazon basin, with an estimated area of 55,069 km². In this area, the mining of Al, Au, Cu, Fe, Ni, and Mn ores began more than 50 years ago 10 , 11 , 12 , 13 , 14 . However, seven protected areas have been created strategically within the area affected by the CMP, including areas of both sustainable use and integral protection. This prolonged, and often unregulated exploitation of natural resources has generated a series of environmental impacts, such as the contamination, destruction, and the silting of rivers and reservoirs, as well as the transportation of metals to aquatic systems, resulting in immeasurable damage to the local aquatic fauna 15 , 16 , 17 . While changes in the structure of local fish communities are predicted in theory, few studies have addressed specifically the potential drivers of shifts in community structure in the region. In general, the impacts caused by mining in the Brazilian Amazon are still poorly understood, and are often overlooked completely in environmental licensing, given that the deforestation associated with the mining tends to exceed greatly the limits of the operational lease 18 . In this context, the impacts may be even greater when considering the dynamics of bodies of water and the leaching process, given that waste can reach both surface and groundwater sources, compromising their quality 19 . Given this, the decline in freshwater biodiversity resulting from contamination of this type has become a global challenge 20 . Reliable data on aquatic diversity will be essential to decipher a number of different ecological processes, as well as the biomonitoring of the health of ecosystems 21 . The effects of mining on aquatic biodiversity depend on the level of pollution, pollutant concentrations, and the nature of the contaminant. In addition, while some species may be resistant to anthropogenic disturbance, others may disappear completely from polluted areas 22 . The lack of studies on the ichthyofauna in Carajás and the progressive degradation of this region by mining over the past few years, highlight the urgent need for research to clarify the current status of its aquatic systems and the potential loss of biodiversity in the region, even considering the limited historical records of species from the area. In this context, we tested the hypothesis that basins near areas impacted by mining operations have lower fish diversity and abundance than preserved areas. To test this hypothesis, the present study assesses fish abundance and diversity (including the composition of assemblages, alpha (α) and beta (β) diversity, the presence of indicator species, and the Species Contribution to b Diversity - SCBD) in impacted and control areas of the Itacaiúnas and Parauapebas basins, during the dry and rainy seasons. Methods Study area The study area comprises three municipalities (Canaã dos Carajás, Marabá, and Parauapebas) located in the Carajás Integration Region, which cover a total area of 25,159.997 km² and share similar characteristics in terms of their urban development and socioeconomic parameters 23 , 24 . This region includes two river basins, associated with the Itacaiúnas and Parauapebas rivers. The region has a hot and humid climate, classified as Aw (tropical savanna climate with a dry winter) in the Köppen system, with a rainy season from December to May (mean precipitation of approximately 1,550 mm) and a dry or less rainy season from June to November (mean precipitation of around 350 mm), with temperatures ranging from 25.1°C to 26.3°C 25,26,27,28,29 . The predominant vegetation in the region is dense rainforest, with tree species that can exceed 50 meters in height, and an abundance of lianas and palms. On a smaller and more seasonal scale, the region also features a deciduous shrubby-herbaceous type of vegetation, typical savanna or steppe, known locally, as canga 30 . The Itacaiúnas River Basin (IRB) is one of the principal tributaries of the Tocantins River. With an area of approximately 41,500 km², it encompasses eleven municipalities in the Brazilian state of Pará and has 39 tributaries. The Itacaiúnas is classified as a fifth-order river, with its headwaters being formed by the confluence of the Água Preta and Azul rivers in the Serra da Seringa (located in the municipality of Água Azul do Norte, in Pará State), and its mouth is located on the left margin of the Tocantins River, in the municipality of Marabá, in Pará 31 , 32 , 33 , 34 , 35 , 36 . Between the 1970s and 2010, approximately 50% of the IRB area was deforested, due primarily to the expansion of farming and ranching, and mining 37 . About one third of the basin lies within legally protected areas, including both conservation units and indigenous lands. The region also has open-pit mines and is considered a major mineral province with considerable long-term potential for the mining of ores, which further highlights its ecological importance and need for the conservation of the associated ecosystems 38 , 39 , 40 . The total production of ore and other goods in the IRB account for 25% of the Gross Domestic Product (GDP) of the Brazilian state of Pará 41 . The Tapirapé River, a major tributary of the Itacaiúnas, is not located in the vicinity of the mining complex, and its basin is adjacent to a conservation unit – the Tapirapé Biological Reserve (REBIO Tapirapé) – which has an area of 103,000 ha and provides full protection for the local fauna and flora. Most of this reserve is located within the municipality of Marabá 42 , 43 , 44 . The Águas Claras stream is located in the central nucleus of the Carajás National Forest, where a pilot plant was established to test ore processing methods for the Igarapé Bahia gold (Au) mining project. This site consists of a small open-pit mine and a pile of rock waste. The Azul River, another tributary of the Itacaiúnas River, has been facing significant environmental threats from illegal gold mining, which has prompted debate among the environmental authorities 45 , 46 . The Cinzento Stream, also known as the Cinzento River, is located in the Parauapebas basin, specifically within the Cinzento Shear Zone, which is part of the Carajás Mineral Province. The Cinzento flows through mineral-rich areas containing ores such as Fe, Cu, and Au, and is part of the network of streams and rivers located within the IRB 47 . The Parauapebas River Basin (PRB) has an estimated total area of 9,604.42 km², encompassing six municipalities. It contains a mineral-rich portion of the Carajás Mineral Province, where major mining projects are located, such as the Sossego Mine (Cu), S11D (Fe), and Serra Leste (Au). This basin is also the primary source of the water supplied to almost the entire municipality of Parauapebas 48 , 49 , 50 . The Gelado Creek is a tributary of the Parauapebas and is approximately 60 km long. It is delimited to the southeast by the Carajás Railroad, to the northeast by the Mombaça and Gelado creeks, to the northwest by the Azul and Itacaiúnas rivers, and to the south by the Carajás National Forest. This creek lies within the Igarapé Gelado Environmental Protection Area, a sustainable-use conservation unit that includes both public and private lands. This stream is located in the vicinity of major mining operations, and falls within the boundaries of the Creek Gelado Environmental Protection Area, APA Igarapé Gelado 51 , 52 , 53 , 54 . The Sossego Mine is located in the municipality of Canaã dos Carajás, near the Parauapebas River, and has been in operation since 2004. This site includes the Sossego (Cu) and S11D (Fe) projects, the latter being considered to be one of the largest iron ore mines in the world 55 . Biological sampling The samples were collected from both altered (impacted) and preserved (control) areas during the rainy (May) and dry (November) seasons, with each campaign lasting 15 consecutive days. Sampling sites were selected based on specific criteria of the degree of anthropogenic disturbance caused by both legal and illegal mining activities, as well as their proximity to urban areas and outlets of sewage discharge. Areas adjacent to mining operations were considered to be altered (impacted), while areas isolated from mining activity were classified as preserved (control areas). Four sampling sites were selected in the IRB and four in the PRB. Two control (blue) and two impacted sites (red) were selected in each basin (Fig. 1 ; Supplementary Table-S1). Fish were captured using gillnets of varying mesh sizes (90 mm, 50 mm, 40 mm, and 35 mm), arranged from the most selective (largest mesh) to the least selective in the direction of the current, and left in the water for 12 hours (from 6:00 p.m. to 6:00 a.m.). Once retrieved, the fish were placed in labeled plastic bags and stored in ice-filled thermal boxes. In the laboratory, the specimens were identified to the lowest possible taxonomic level 56 , 57 , 58 , 59 , and subsequently measured (total length, in mm) and weighed (total weight, in g). The physicochemical parameters of the water (pH, conductivity, temperature, and salinity) were recorded twice (at 6:00 p.m. and 6:00 a.m.) at each sampling site using a multiparameter probe (HANNA HI 98194), during the biological sampling. Samplings were authorized by the Chico Mendes Institute for Biodiversity Conservation, SISBIO license no 75796-4 and approved by the Ethics Committee on the Use of Animals at the Federal University of Pará (CEUA/UFPA), protocol no. 8587260821. Statistical analysis All the analyses were run in the R software (R Core Team, 2023) 60 . Prior to analysis, the data were examines to verify the presence of outliers, the homogeneity of variance, and the normality of the residuals, as well as the potential linearity and collinearity among the variables, following the protocol proposed by Zuur et al. (2010) 61 . The environmental variables (conductivity, salinity, and temperature) were compared spatiotemporally (season: dry and rainy; area: impacted and control; basin: IRB and PRB), as well as in terms of their interaction with the basins, using PERMANOVA. These environmental interactions were then visualized using a Principal Components Analysis (PCA). The abiotic data matrix was standardized prior to this analysis using the Z-score method, due to the different measuring units and scales of the variables analyzed 62 . Collinearity was assessed using the Variance Inflation Factor (VIF), and all the variables with VIF > 3 (e.g., pH) were removed, following the approach proposed by Zuur et al. (2007, 2010) 63 , 64 . Prior to these analyses, a species accumulation curve was constructed to assess the sampling efficiency of the study. The total abundance and alpha diversity (α; measured by species richness) of the fish assemblage were analyzed separately using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), with negative binomial type I and II distributions, respectively. GAMLSS are semiparametric models in which the distribution of the response variables (continuous or discrete) may be asymmetric, and are commonly used to analyze complex data 65 . The distribution of the response variables was evaluated individually using the “fitDist” function of the “gamlss” package, based on the Generalized Akaike Information Criterion 66 . The “stepGAICAll.A” function was then used to build each model, starting from the intercept and then selecting variables step-by-step, based on the reduction of the GAIC. The final model retained only the variables that had a significant influence on the response variable. The composition and β diversity (based on the Whittaker method) of the fish assemblage were analyzed individually in relation to spatiotemporal factors (season, area, basin, and their interactions) using PERMANOVA, which was adjusted by the Bonferroni correction, through the "adonis2" function of the "vegan" package 66 . Here, the matrix of biotic data was standardized using the Hellinger method, as recommended by Legendre and Legendre (2012) 67 . The relationships among the variables were then visualized using a Principal Components Analysis (PCA). Beta diversity was calculated using the "betadiver" function of the "vegan" package [68] based on Whittaker’s method (βw), as proposed by Kollef et al. (2003) 69 . According to these authors, this method broadly captures the assemblage turnover, which reflects species replacement and results in high values when there are marked differences in the composition of the species. The indicator species were identified using the "IndVal" function of the "labdsv" package 70 for each spatiotemporal factor (season, area, basin, and their interactions). This analysis was based on the IndVal index, which combines the relative abundance of the species with their frequency across the samples, and ranges from 0 (no association) to 1 (exclusive occurrence of a species in each factor) 71 . The SCBD was also evaluated spatiotemporally (season, area, and basin), using the "beta.div" function of the vegan package 72 . For this, the biotic matrix was previously standardized using the Hellinger method, and only comparisons within each factor resulting in differences greater than |0.03| were considered relevant. Finally, a Canonical Redundancy Analysis (RDA) was run to assess the influence of the abiotic matrix on the biotic matrix. The significance of the model and its axes was determined using the “permutest” and “anova” functions, respectively, and the significance of the relationships between the environmental and biotic variables extracted from the RDA was determined using the "envfit" function. The "adonis2", “permutest”, "anova", and “envfit" functions were all run with 9,999 permutations. Results Environmental Characteristics Water temperature, conductivity, and salinity were all significantly higher in the dry season (PERMANOVA: F = 9.10; p = 0.0005; Fig. 2 a). The highest temperatures were recorded in the impacted areas, while the highest salinity was recorded in the control areas (PERMANOVA; F = 3.51; p = 0.0243; Fig. 2 b). The lowest mean values for all these parameters were recorded in the IRB (PERMANOVA; F = 39.40; p = 0.0001; Fig. 2 c). In the PRB, the highest mean temperature average was recorded in the rainy season, while in the highest conductivity and salinity were recorded during the dry season (PERMANOVA; F = 4.60; p = 0.0091; Fig. 2 d). While the highest temperatures were recorded in impacted areas in the PRB, the lowest mean salinity and conductivity were recorded in the control areas (PERMANOVA; F = 6.57; p = 0.0018; Fig. 2 e). Composition and abundance of the fish assemblage A total of 766 fish specimens were collected during the present study, representing two classes, nine orders, 22 families, and 59 species across all sites during the two seasons (see Supplementary-Table S2). The order Characiformes was the most diverse, with nine families and 14 species, followed by the Siluriformes with four families and 15 species, and the Cichliformes with one family and 10 species. The most abundant species were Satanoperca jurupari (Heckel, 1840), with 101 individuals; Ageneiosus inermis (Linnaeus, 1766) and Eigenmannia sp . (Jordan and Evermann, 1896), both with 69 individuals; Leporinus friderici (Bloch, 1974), with 60 individuals; and Plagioscion squamosissimus (Heckel, 1840) and Hydrolycus tatauaia (Toledo-Piza, Menezes &Santos, 1999), each with 33 individuals. The species accumulation curve demonstrated that the sampling effort was sufficient for the study assemblage, given that the composition did not vary significantly between seasons, areas, or their interactions with the basins (PERMANOVA; p > 0.05; Fig. 3 a, b, d, e). However, the fish assemblage of the IRB basin was composed primarily of Leporinus friderici (n = 60), Hydrolycus tatauaia (n = 33), Plagioscion squamosissimus (n = 33), and Pellona castelnaeana (Valenciennes, 1847) (n = 5), while Tometes sp . (Valenciennes, 1850), Cichla ocellaris (Bloch & Schneider, 1801), Cynopotamus juruenae (Menezes, 1987), and Auchenipterus nuchalis (Spix & Agassiz, 1829), each represented by a single specimen, were all exclusive to the PRB basin (Fig. 3 c). Total fish abundance was higher during the rainy season (GAMLSS; p = 0.0003; Supplementary Table S3; Fig. 4 a). No significant differences were observed between the areas, basins or their interactions (GAMLSS; p > 0.05; Supplementary Table S3; Fig. 4 b, c, d, e). Myloplus rubripinnis (Müller and Troschel, 1844) (IndVal = 0.3219), Hypostomus plecostomus (Linnaeus, 1758) (IndVal = 0.3212), and Myleus sp . (IndVal = 0.2500) were identified as indicators of the dry season (Table 1 ). Leporinus friderici was the indicator species for the IRB (IndVal = 0.7100), although no indicator species was identified for the PRB (Table 1 ). In the interaction between seasons and basins, only Leporinus friderici (IndVal = 0.6542), Hydrolycus tatauaia (IndVal = 0.4000), Pellona castelnaeana (IndVal = 0.4000), and Serrasalmus manueli (Fernández-Yépez & Ramírez, 1967) (IndVal = 0.3636) were identified as indicators of the dry season in the IRB basin. However, no species were associated with any of the other interactions (Rainy: IRB, Dry: PRB, and Rainy: PRB; Table 4). In the interaction between areas and basins, Plagioscion squamosissimus (IndVal = 0.3666) was the indicator species of the control points in the IRB basin, Pygocentrus nattereri (Kner, 1858) (IndVal = 0.3499) of the control points in the PRB basin, and Satanoperca jurupari (IndVal = 0.4972) of the impacted points in the PRB basin (Table 1 ). No indicator species were identified for the two types of area (control vs. impacted). Table 1 Indicator fish species (IndVal) identified at the study sites in the Itacaiúnas (IRB) and Parauapebas River basins (PRB), and their interactions with the control and impacted areas during the dry and rainy seasons. Values in bold script indicate significance differences (p < 0.05). Indicator Species Group IndVal% p Myloplus rubripinnis Dry 0.3219 0.020 Hypostomus plecostomus Dry 0.3212 0.042 Myleus sp. Dry 0.2500 0.041 Leporinus friderici IRB 0.7100 0.003 Leporinus friderici Dry IRB 0.6542 0.004 Hydrolycus tatauaia Dry IRB 0.4000 0.026 Pellona castelnaeana Dry IRB 0.4000 0.032 Serrasalmus manueli Dry IRB 0.3636 0.035 Plagioscion squamosissimus Control IRB 0.3666 0.045 Pygocentrus nattereri Control PRB 0.3499 0.038 Satanoperca jurupari Impacted PRB 0.4972 0.005 Alpha and Beta Diversity Alpha diversity (α), as measured by species richness, was significantly higher during the rainy season (GAMLSS; p = 0.0019; Table 2 ; Fig. 5 a). However, no significant variation was observed between areas, basins, or their interactions (GAMLSS; p > 0.05; Table 2 ; Fig. 5 b, c, d, e). In fact, there was little variation between seasons, areas or their interactions (PERMANOVA; p > 0.05; Supplementary Table S4; Fig. 6 a, b, d, e), although higher values of β diversity were observed in the PRB in comparison with the IRB (PERMANOVA; F = 2.163; p = 0.0112; Supplementary Table S4; Fig. 6 c). Table 2 Variation in the alpha diversity (α) of the fish species between seasons (dry and rainy-A), areas (control and impacted -B), basins (IRB and PRB-C), seasons and basins (dry:IRB; dry:PRB; rainy-IRB; rainy-PRB-D), areas and basins (impacted:IRB; impacted:PRB; reference:IRB; reference:PRB-E) in the Carajás Mineral Province, Brazil, in May 2022, and November 2022. *indicates a significant difference among the factors (p < 0.05). Univariate model: Alfa diversity ~ 1, sigma.formula = ~ Season; Family = Binomial Negative Type II; AIC = 224,802; R²=22,44% Mu coefficient Estimate Std. Error t p Intercept 1.3213 0.1373 9.626 1.08e-12 Sigma coefficient Estimate Std. Error T p Intercept 0.5696 0.4873 1.169 0.2484 Season 2.0875 0.6366 3.279 0.0019 Table 3 Species Contributions to Beta (β) Diversity (SCBD) in the fish assemblage between rainy and dry seasons, control and impacted areas, and the IRB and PRB basins in the Carajás Mineral Province, Brazil, in May and November 2022. Values in bold script indicate a significant contribution to the SCBD, and the asterisks (*) denote the highest values in each comparison. Species Season Areas Basin Rainy Dry Control Impacted IRB PRB Eigenmannia sp. 0.1320*0.0473 Satanoperca jurupari 0.02280.0870* 0.05590.1652* 0.00530.1034* 0.1700*0.0446 0.00000.0818* In the analysis of the Species Contribution to β Diversity (SCBD) of the fish assemblages (Supplementary-Table S5), Eigenmannia sp. presented the highest values during the rainy season (0.1320), in impacted areas (0.1652), and in the IRB (0.1700), whereas Satanoperca jurupari presented the greatest contribution in the dry season (0.0870), in impacted areas (0.1034), and in the PRB (0.0818) (Table 3). Relationship between fish species abundance and environmental variables The water temperature, conductivity, and salinity were all related significantly to the abundance of fish species, although only the first RDA axis was significant, explaining 46.32% of the variability in the data (F = 2.16; p = 0.03; Fig. 7 ). Crenicichla ocutirostris (Günther, 1862), Prochilodus nigricans (Spix & Agassiz, 1829), Pygocentrus nattereri , and Pimelodus sp. (Lacépède, 1803) all presented a positive relationship with salinity, conductivity, and temperature. By contrast, Leporinus friderici, Plagioscion squamosissimus, Eigenmannia sp., Geophagus neambi (Lucena & Assis, 2010), Retroculus lapidifer (Castelnau, 1855), and Satanoperca jurupari were associated negatively with these variables. While no significant variation in abundance was found between basins, fish were clearly more abundant in the PRB basin during the rainy season (Fig. 8 ). Discussion Phenotypic plasticity The phenotypic plasticity of the fish assemblage studied here may account for the considerable similarities in total abundance, species composition, and taxonomic diversity found between impacted and control areas, and between the two study basins. This mechanism is defined as the capacity of a genotype to exhibit varying responses to shifts in the environment 73 . In aquatic environments, this plasticity is expressed through physiological, morphological, and behavioral adjustments, including changes in the tolerance of the species to reduced water quality, its habitat use, diet, and bodily proportions 74 , 75 , 76 . A number of different studies have shown that tropical fish have a considerable potential for physiological adjustment in response to environmental stressors such as changes in salinity, temperature, pH, and oxygen availability, which may help to explain, in part, their persistence in environments impacted by mining 77 , 78 . In particular, increased exposure to metals and high conductivity, conditions that are typical of areas affected by mining, may trigger compensatory physiological mechanisms. Tolerance of salinity, for example, has been associated with the plasticity of ion transport mechanisms in Neotropical fish, which can contribute to their survival in degraded environments 79 , 80 . These traits increase the likelihood of survival for the more tolerant species, allowing them to persist even under extremely altered environmental conditions 81 , 82 , 83 . However, while phenotypic plasticity may underpin the capacity of communities to persist in the short to medium term, it should not be interpreted as a sign of ecological stability, given that it may mask losses in functional diversity and long-term resilience 84 ,85 . In fact, threshold analyses of the tolerance of fish in watersheds in the eastern United States revealed significant negative effects of mining on the diversity and evenness of the fish assemblages, indicating that the mines act as regional sources of disturbance 86 . Similarly, although the evidence from the present study indicates some resilience in the fish populations exposed to impacts from mining, the accumulated evidence indicates that, in regions with intense and persistent mining operations, the impacts on the local aquatic communities are both significant and multifaceted. In Wabush Lake (Canada), iron (Fe) tailings caused a vitamin A deficiency in lake trout ( Salvelinus namaycush ), which led to skin whitening syndrome 87 . In Brazil, fish exposed to water contaminated by the Fundão dam tailings (in Mariana, Minas Gerais state) presented moderate to severe histological damage of the gonads, which may compromise reproductive processes over the long term 88 . Significant histopathological alterations were also observed in the gills of Astyanax aff. bimaculatus exposed to high concentrations of zinc, Zn 89 . The results of the present study indicate that tolerance mechanisms, such as phenotypic plasticity, may play a central role in the apparent resilience of the fish assemblage of Carajás, a region that has been impacted profoundly by large-scale mining operations for more than three decades. However, this may mask deeper and progressive changes in the structure of the local fish communities, especially over the long term, which reinforces the need for further, continuous monitoring, including the assessment of functional diversity, the physiological health of the organisms, and environmental quality. Temporal Patterns The greater abundance and taxonomic diversity observed during the rainy season, together with the lack of indicator species exclusive to this period, indicate that the species that make up the local assemblage are mostly opportunistic generalists. These species can benefit from the temporary increase in the availability of food and shelter during this period, without necessarily establishing patterns of ecological exclusivity. In fact, many of the species recorded during the rainy season also occur in the dry season, albeit at a lower abundance, reflecting survival strategies that encompass varying environmental conditions. Although no indicator species were detected during the rainy season, the environmental conditions during this period favor the entry of individuals of many different species, increasing the total number of individuals without any single species reaching the threshold considered to be typical of an indicator species. As river levels rise during the rainy season, aquatic habitats expand and the connectivity between environments increases, facilitating the entry of many species into the floodplain 90 . This increased connectivity may also promote homogenization of the community and thus reduce species dominance, making it more difficult to detect any indicator species 91 , 92 , 93 . Similar patterns have been observed in tropical streams 94 ,95 and in the Esa-Odo Reservoir in Spain 96 . However, very different patterns have also been reported in studies in other areas, including streams in Central Amazonia 97 , the Piagaçu-Purus Sustainable Development Reserve on the Purus River in the Brazilian state of Amazonas 98 , and in lakes in Nigeria 99 , where higher fish abundance was observed during the ebb season, in contrast with the findings of the present study. In Manaus (Amazonas state, Brazil), Espírito-Santo et al. (2009) 100 found no significant variation in fish abundance between seasons in protected areas. The dominance of M. rubripinnis, H. plecostomus , L. friderici and Myleus sp . as indicator species for the dry season, appears to be associated with their physiological tolerance, interactions with abiotic parameters, feeding plasticity, and ecological adaptations, which result in distinct behavioral responses to seasonal variation. Myloplus rubripinnis , Myleus sp ., and L. friderici have herbivorous/frugivorous feeding niches, and during the dry season, these species can be found in isolated pools or channels, where they feed primarily on aquatic vegetation and the detritus that accumulates during periods of low water [101,102,103]. Hypostomus plecostomus is able to tolerate both high temperatures and hypoxia, and in the dry season, it is able to alter ecosystem structure through the reduction of the periphyton and the promotion of the cycling of nutrients, such as phosphorus (P). At high densities, then, these species may all act as nutrient sinks 104 ,105 . The identification of P. squamosissimus as an indicator specie for the control area in the IRB indicates that, while it is able to tolerate variation in the environment, it tends to be more successful in less disturbed environments. The feeding habits and reproductive behavior of this species make it a valuable bioindicator of environmental quality. As a carnivore, which feeds primarily on crustaceans and small fish, P. squamosissimus is commonly found in environments where these resources are abundant 106 , 107 . Moreover, P. squamosissimus presents reproductive habits that are synchronized with the hydrological regime, relying on seasonal flooding to guarantee its reproductive cycle, including the recruitment of juveniles, in addition to hydrologically intact systems with preserved areas of floodplain 108, 109 , 110 . While P. squamosissimus presents some degree of phenotypic plasticity 111 , then, there are ecological limits to its persistence in degraded environments. Rocha et al. (2016) 112 investigated the cytotoxicity and mutagenicity of the waters of the Marajó archipelago (Pará, Brazil) using P. squamosissimus as a bioindicator, and found that exposure to pollutants can induce genetic alterations in this species. Oliveira et al. (2022) 113 also reported genotoxic effects in P. squamosissimus in Amazon estuaries impacted by mining, using this species as a biomonitor, and observed that the fish examined were influenced directly by xenobiotic agents, which also impacted their genetic material. Species that appear exclusively or preferably during the dry season tend to seek out more stable and isolated environments that create well-defined habitats, in which certain species are confined and become rare or absent during the rainy season, which can result in significant IndVal values. The identification of Pygocentrus nattereri as an indicator species for the control areas in the PHRB basin may be related to its greater abundance in areas with reduced forest cover. These areas generally provide readier access to feeding resources, which is essential for the growth and reproduction of the species 114 . This pattern reflects the tolerance of these species of structurally simpler and more open environments, which is a common trait in the generalist species that are able to exploit a variety of niches and persist in areas subject to moderate anthropogenic disturbance 115 , 116 , 117 . In addition, P. nattereri may benefit from relatively stable and preserved ecological conditions, given that less impacted environments tend to have a greater availability of prey, better reproductive conditions and adequate shelter, all of which favor the ecological performance of the species 118 , 119 . It is also well established that generalist species tend to have greater eurytopy in comparison with more specialist species 120 . The high water temperatures recorded during the rainy season in the PRB were influenced by the controlled release of surface water by the mining company at one of the sampling points. In fact, the increased availability of food and habitats during the rainy season may also favor the general persistence of fish in Carajás. In studies of streams in the United States, Daniel et al. (2015) 121 reported that mining has negative effects on the diversity and evenness of fish assemblages, as well as the number of taxa with specific strategies, and trophic and habitat preferences. Even so, the absence of detectable changes in the fish diversity of Carajás, despite the duration of the present study, likely reflects a process of biotic homogenization, in which the more sensitive species are replaced by more tolerant ones, thereby maintaining species richness, while reducing functional and phylogenetic diversity, although the latter two parameters were not examined in the present study. The fish assemblage The composition and stability of the fish assemblage varied between the two study basins, with the PRB having a higher species turnover (β-diversity), despite the lack of difference in total abundance or alpha diversity. No indicator species were found in the PRB, although the IRB assemblage did appear to be more stable, driven by the consistent presence and relative abundance of L. friderici . Furthermore, species turnover in the PRB may have been influenced by the hydrological conditions predominating during the rainy season, with Eigenmannia sp . contributing most to the SCBD of the β-diversity in the impacted areas during the rainy season. The species of the order Gymnotiformes are widely used as bioindicators of environmental quality due to their specific physiological and behavioral characteristics, such as the waveform, amplitude, and repetition rate of their Electric Organ Discharges (EOD). These parameters are directly linked to the physicochemical properties of water, especially its electrical conductivity, ion concentration, and the presence of contaminants, and can be altered significantly in response to environmental stressors 122, 123 . The presence of toxic elements in the water, such as metals, may affect both the production and modulation of electric signals, which can impair essential functions such as communication, orientation, and foraging, in addition to impacting the energetic metabolism of these fish. The environmental characteristics observed in the IRB basin, combined with the eco-physiological sensitivity of Eigenmannia sp ., may explain its irregular occurrence or more frequent replacement in the impacted areas. This indicates that the species responds negatively to changes in water quality, potentially being excluded or displaced from environments under diffuse pollution or significantly altered conductivity. This species may thus represent a sensitive biological indicator of local conditions. Although the PRB did not have any exclusive indicator species, Satanoperca jurupari contributed most to species turnover during the dry season in the impacted areas. A positive relationship was also observed between the abundance of both C. ocutirostris and P. nigricans with water temperature, which indicates that these species have physiological adaptations to warmer environments, such as an elevated basal metabolism. In ectothermic fish, such as teleosts, the ambient temperature regulates directly the metabolic rate, that is, as the temperature increases, so does the demand for energy, leading to a higher respiratory rate, oxygen consumption, and excretion of ammonia (NH₃), which is the primary byproduct of protein metabolism 124 . Ammonia is excreted primarily through the gills, via passive diffusion and active transport, and excessive production of this compound can disrupt the acid-base balance of the organism, which requires complex physiological adjustments, especially in warm environments with reduced oxygen concentrations 125 , 126 . The presence of P. nigricans and C. ocellata in warmer locations, as observed in the present study, may reflect their capacity to withstand these increased physiological demands, which favors their persistence in areas with higher surface water temperatures. As highlighted by Azzurro (2008) 127 , species with amplified thermal tolerance tend to expand into or dominate warmer environments, which are often associated with anthropogenic disturbance, thereby promoting alterations in the structure of aquatic communities. The positive relationship between P. nigricans and salinity indicates a possible adaptation associated with its migratory habits, which is a common pattern in the Neotropical fish that move between environments that vary in their salt concentrations 128, 129 ,130, 131 . In Amazonian environments, the fluctuation in water levels over the course of the hydrological cycle leads to significant variation in the physicochemical parameters of the water, including its dissolved salt concentration, which demands considerable ecological and physiological flexibility from the resident fish. In the context of mining in the Carajás region, the localized increase in salinity may be related to mineral leaching and alterations in the hydrological regime provoked by mining activities, which can contribute to an increase in the electrical conductivity of the water, which is often used as an indirect indicator of dissolved ion (salt) concentrations. Given this, the presence of P. nigricans in areas with higher salinity may reflect both the physiological tolerance of this species and its adaptive behavioral response to disturbed environments. The observed relationship between Pygocentrus nattereri and conductivity indicates that this species is tolerant to environmental stressors, given that elevated conductivity reflects the presence of pollutants or dissolved solids in the water 132 . This tolerance reflects the ability of P. nattereri to persist in environments with significant chemical and physical variations, which are common in areas impacted by activities such as mining. However, while this plasticity allows for the survival of the species, continuous exposure to these stressors may have a negative effect on the stability of the population and, in turn, that of the aquatic community as a whole. Clearly, the continuous monitoring of the physiological and population responses, especially of the indicator species identified in the present study, is fundamental to the understanding of the cumulative impacts of environmental pressures and, in the specific case of Carajás, one potential measure would be too make mining companies responsible for providing specimens periodically for analysis, based on a systematic sampling protocol. These analyses should be funded by the mining companies, but conducted by universities, as a condition for the licensing of mining operations. An approach of this type, in addition to establishing more effective programs for the control of contamination, would greatly enhance long-term data cataloging, the training of personnel, and the optimization of analytical methods. Declarations Conflict of interest: None of the authors declares any conflict of interest. Ethics Statement The study did not involve animal experimentation in the laboratory. All fish analyzed were collected exclusively from the natural environment, and all procedures were conducted under the appropriate ethical and legal authorizations, by the Chico Mendes Institute for Biodiversity Conservation, SISBio license no 75796-4 (see supplementary S6) and approved by the Ethics Committee on the Use of Animals at the Federal University of Pará (CEUA/UFPA), protocol no. 8587260821 (see supplementary S7). Consent to participate: All the authors declare consent. Consent for publication: All the authors declare consent. Funding Research funded by Cooperation Agreement No. 04/2018 between ICMBio, Salobo Metais S.A and Funtec-DF. This research is mainly supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil), through the Postgraduate Program in Aquatic Ecology and Fisheries (PPGEAP), Center for Aquatic Ecology and Fisheries of the Amazon (NEAP), Federal University of Pará (UFPA, Brazil). Author Contribution CACRO: Project administration, collection of samples in the field, Writing - Original draft, Methods, Investigation, Formal analysis, Data curation, Formal analysis; KSM: Writing – Formal analysis; Assistance in writing and reviewing the manuscript, Data curation; JPSO: Writing- Assistance in writing and reviewing the manuscript; AOM: Assistance in writing and reviewing the manuscript, Statistical analysis of the data; ABFS: Writing - Assistance in writing and reviewing the manuscript; DCP: Collection of samples in the field; BB: Collection of samples in the field, Supervision, Review & editing, Funding acquisition, Conceptualization. Acknowledgement We would like to thank the Chico Mendes Institute for Biodiversity Conservation (ICMBIO) for funding the project that generated the data for this study and for all the logistical support during the collection campaigns in the Carajás Mosaic. Author CACRO would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil) for its support (Phd scholarship - Proc 495 88887.082622/2024-00). Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Checon, H. H., Costa, H. H. R., Corte, G. N., Souza, F. M. & Pombo, M. Rainfall Influences the Patterns of Diversity and Species Distribution in Sandy Beaches of the Amazon Coast. Sustainability 15 , 5417 (2023). Correa, S. B. et al. Floodplain forests drive fruit-eating fish diversity at the Amazon Basin-scale. Proceedings of the National Academy of Sciences 122, (2025). Garcia, A. et al. Biodiversity hotspots and threatened species under human influence in the Amazon continental shelf. Scientific Reports 15 , (2025). Tickner, D. et al. Bending the Curve of Global Freshwater Biodiversity Loss: An Emergency Recovery Plan. BioScience 70, 330–342 (2020). 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C., Hashim, M., Ismail, S. & Shehu, S. A. Modeling pH changes and electrical conductivity in surface water as a result of mining activities. Naukovij vìsnik Nacìonalʹnogo gìrničogo unìversitetu . 122–129. https://doi.org/10.33271/nvngu/2024-1/122 (2024). Captions. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":375176,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the sampling points at which fish specimens were collected in the \u0026nbsp;Carajás Mineral Province, Brazil, in May and November 2022. (\u003cstrong\u003eA\u003c/strong\u003e) – Points 1 and 3 are the control sites, and points 2 and 4, the impacted sites in the Itacaiúnas Basin, while points 5 and 8 are impacted, and points 6 and 7 are the control sites in the Parauapebas Basin. (\u003cstrong\u003eB\u003c/strong\u003e) – Municipalities located within the study region: Marabá, Parauapebas, and Canaã dos Carajás. (\u003cstrong\u003eC\u003c/strong\u003e) – State of Pará. (\u003cstrong\u003eD\u003c/strong\u003e) – Brazil.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/d4c5ffcbe741602cc2c74080.png"},{"id":95386496,"identity":"262642f8-b3cd-4ea2-bf8b-ac36de296567","added_by":"auto","created_at":"2025-11-07 13:02:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157572,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the variation in the physicochemical parameters (temperature, salinity, and conductivity) of the water between: (\u003cstrong\u003ea\u003c/strong\u003e) the dry and rainy seasons; (\u003cstrong\u003eb\u003c/strong\u003e) the impacted and control areas; (\u003cstrong\u003ec\u003c/strong\u003e) the IRB and PRB basins, and (\u003cstrong\u003ed\u003c/strong\u003e) the interaction between seasons and basins (dry: IRB, dry: PRB, rainy: IRB, rainy: PRB) and (\u003cstrong\u003ee\u003c/strong\u003e) the interaction between areas and basins (impacted: IRB, impacted: PRB, control: IRB, control: PRB) in the Carajás Mineral Province, Brazil, in May and November 2022. *Indicates a significant difference among the factors (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/f1ff5d41b11207fd7c0f52d7.png"},{"id":95386498,"identity":"783888a7-9e8b-4abb-be8b-13a5db6671e4","added_by":"auto","created_at":"2025-11-07 13:02:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":383184,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in the composition of the fish assemblage between: (\u003cstrong\u003ea\u003c/strong\u003e) the rainy and dry seasons; (\u003cstrong\u003eb\u003c/strong\u003e) the control and impacted areas; (\u003cstrong\u003ec\u003c/strong\u003e) the IRB and PRB basins, and (\u003cstrong\u003ed\u003c/strong\u003e) the interaction between seasons and basins (dry: IRB, dry: PRB, rainy: IRB, rainy: PRB); (\u003cstrong\u003ee\u003c/strong\u003e) interaction between areas and basins (impacted: IRB, impacted: PRB, control: IRB, control: PRB) in the Carajás Mineral Province, Brazil, in May and November 2022. *Indicates a significant difference among the factors (α\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/c5be973df2b4a470f4622ec7.png"},{"id":95526170,"identity":"07495dcb-e11f-4dd3-82ce-169bb5ee3cb4","added_by":"auto","created_at":"2025-11-10 10:06:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46362,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in the total abundance of fish between: (\u003cstrong\u003ea\u003c/strong\u003e) the dry and rainy seasons; (\u003cstrong\u003eb\u003c/strong\u003e) the control and impacted areas; (\u003cstrong\u003ec\u003c/strong\u003e) the IRB and PRB basins, and (\u003cstrong\u003ed\u003c/strong\u003e) the interaction between seasons and basins (dry: IRB; dry: PRB; rainy: IRB; rainy: PRB), (\u003cstrong\u003ee\u003c/strong\u003e) the interaction between areas and basins (impacted: IRB; impacted: PRB; reference: IRB; reference: PRB) in the Carajás Mineral Province, Brazil, in May and November 2022. *Indicates a significant difference among the factors (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/169ab470556c79d29f83f619.png"},{"id":95386501,"identity":"2ea89191-101d-4e6f-a222-dcbbea0907e3","added_by":"auto","created_at":"2025-11-07 13:02:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49233,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in the alpha diversity (\u003cstrong\u003eα\u003c/strong\u003e) of the fish assemblages between: (\u003cstrong\u003ea\u003c/strong\u003e) the dry and rainy seasons, (\u003cstrong\u003eb\u003c/strong\u003e) the control and impacted areas, (\u003cstrong\u003ec\u003c/strong\u003e) the IRB and PRB basins, and (\u003cstrong\u003ed\u003c/strong\u003e) the interaction between seasons and basins (dry: IRB; dry: PRB; rainy: IRB; rainy: PRB), (\u003cstrong\u003ee\u003c/strong\u003e) the interaction between areas and basins in the Carajás Mineral Province, Brazil, in May and November 2022. *Indicates a significant difference among the factors (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/c04aae1fb259a930225239c8.png"},{"id":95526633,"identity":"cb107db9-d90a-4840-a569-8c5dd0d66a30","added_by":"auto","created_at":"2025-11-10 10:07:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81220,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in the beta diversity (β) of the fish assemblages between: (\u003cstrong\u003ea\u003c/strong\u003e) the dry and rainy seasons, (\u003cstrong\u003eb\u003c/strong\u003e) the control and impacted areas, (\u003cstrong\u003ec\u003c/strong\u003e) the IRB and PRB basins, and (\u003cstrong\u003ed\u003c/strong\u003e) the interaction between seasons and basins (dry: IRB; dry: PRB; rainy: IRB; rainy: PRB), (\u003cstrong\u003ee\u003c/strong\u003e) the interaction between areas and basins in the Carajás Mineral Province, Brazil, in May and November 2022. *Indicates a significant difference among the factors (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/8b9a6b0cde6de0d521fa5e7e.png"},{"id":95386505,"identity":"36c485e1-5c50-4286-ad43-7f95034db8af","added_by":"auto","created_at":"2025-11-07 13:02:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":56001,"visible":true,"origin":"","legend":"\u003cp\u003eOrdination of the Redundancy Analysis (RDA), showing the relationships between the selected abiotic factors (Temp. = temperature; Salin. = salinity; Cond. = conductivity) and the distribution of the fish species collected in the Carajás Mineral Province, Brazil, in May and November 2022.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/320d236b04a1e40652838477.png"},{"id":95525891,"identity":"1bdbdb51-bf50-4064-a626-52271b8dcb9c","added_by":"auto","created_at":"2025-11-10 10:05:49","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":101289,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the variation in mean abundance (indicated by the relative size of the fish symbols), α diversity, and β diversity according to the different factors, i.e., season (dry and rainy), basin (IRB and PRB), and area (IMP = impacted; CON = control), in the Carajás Mineral Province, northern Brazil, in May and November 2022. The symbols represent the differences among the samples. Subtitles: Comparing seasons, abundance was highest during the rainy season in the PRB. Between areas, abundance was highest in the impacted area of the PRB. The asterisk (*) indicates a significant difference among the samples, with abundance, alpha diversity, and beta diversity being higher during the rainy season and at the control sites.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/0384d0d2ad55da469229e455.jpeg"},{"id":104740920,"identity":"5619cf54-7a8c-4c12-9365-2093075656c2","added_by":"auto","created_at":"2026-03-16 16:19:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2699327,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/2d4ca139-49e4-4ce1-92a6-e9d88bcace19.pdf"},{"id":95526088,"identity":"c1d9f911-2b57-40ab-addb-c4dad77ac2a2","added_by":"auto","created_at":"2025-11-10 10:06:14","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1866810,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7887845/v1/b286abee0ac142a8b8a57228.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The use of fish diversity and abundance as environmental indicators in a mining region in Brazilian Amazonia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Amazon basin is widely considered to be a hotspot of fish diversity, given that it is the world\u0026rsquo;s most diverse biome and has the richest freshwater ichthyofauna. However, the Amazon basin has been facing a series of anthropogenic pressures that threaten its future and ecological stability, and have drastically reduced its biodiversity and driven many fish species toward extinction \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe human activities that threaten biodiversity are considered to be critical environmental issues, given that conservation is not only essential for saving species, but also contributes to the preservation of habitats. In the Amazon basin, the consequences of urban growth, unfettered deforestation, the degradation of natural resources, the lack of basic sanitation, squatting, and mining, have caused an enormous range of impacts that affect the aquatic biota directly \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the Amazon region, mining has put even more pressure on areas that are crucial for biodiversity conservation \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e such as the Caraj\u0026aacute;s Mineral Province (CMP), which is the largest reserve of mineral resources in the Amazon basin, with an estimated area of 55,069 km\u0026sup2;. In this area, the mining of Al, Au, Cu, Fe, Ni, and Mn ores began more than 50 years ago \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, seven protected areas have been created strategically within the area affected by the CMP, including areas of both sustainable use and integral protection.\u003c/p\u003e\u003cp\u003eThis prolonged, and often unregulated exploitation of natural resources has generated a series of environmental impacts, such as the contamination, destruction, and the silting of rivers and reservoirs, as well as the transportation of metals to aquatic systems, resulting in immeasurable damage to the local aquatic fauna \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While changes in the structure of local fish communities are predicted in theory, few studies have addressed specifically the potential drivers of shifts in community structure in the region. In general, the impacts caused by mining in the Brazilian Amazon are still poorly understood, and are often overlooked completely in environmental licensing, given that the deforestation associated with the mining tends to exceed greatly the limits of the operational lease \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this context, the impacts may be even greater when considering the dynamics of bodies of water and the leaching process, given that waste can reach both surface and groundwater sources, compromising their quality \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Given this, the decline in freshwater biodiversity resulting from contamination of this type has become a global challenge \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eReliable data on aquatic diversity will be essential to decipher a number of different ecological processes, as well as the biomonitoring of the health of ecosystems \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The effects of mining on aquatic biodiversity depend on the level of pollution, pollutant concentrations, and the nature of the contaminant. In addition, while some species may be resistant to anthropogenic disturbance, others may disappear completely from polluted areas \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe lack of studies on the ichthyofauna in Caraj\u0026aacute;s and the progressive degradation of this region by mining over the past few years, highlight the urgent need for research to clarify the current status of its aquatic systems and the potential loss of biodiversity in the region, even considering the limited historical records of species from the area. In this context, we tested the hypothesis that basins near areas impacted by mining operations have lower fish diversity and abundance than preserved areas. To test this hypothesis, the present study assesses fish abundance and diversity (including the composition of assemblages, alpha (α) and beta (β) diversity, the presence of indicator species, and the Species Contribution to b Diversity - SCBD) in impacted and control areas of the Itacai\u0026uacute;nas and Parauapebas basins, during the dry and rainy seasons.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe study area comprises three municipalities (Cana\u0026atilde; dos Caraj\u0026aacute;s, Marab\u0026aacute;, and Parauapebas) located in the Caraj\u0026aacute;s Integration Region, which cover a total area of 25,159.997 km\u0026sup2; and share similar characteristics in terms of their urban development and socioeconomic parameters \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This region includes two river basins, associated with the Itacai\u0026uacute;nas and Parauapebas rivers.\u003c/p\u003e\u003cp\u003eThe region has a hot and humid climate, classified as Aw (tropical savanna climate with a dry winter) in the K\u0026ouml;ppen system, with a rainy season from December to May (mean precipitation of approximately 1,550 mm) and a dry or less rainy season from June to November (mean precipitation of around 350 mm), with temperatures ranging from 25.1\u0026deg;C to 26.3\u0026deg;C \u003csup\u003e25,26,27,28,29\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe predominant vegetation in the region is dense rainforest, with tree species that can exceed 50 meters in height, and an abundance of lianas and palms. On a smaller and more seasonal scale, the region also features a deciduous shrubby-herbaceous type of vegetation, typical savanna or steppe, known locally, as canga \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Itacai\u0026uacute;nas River Basin (IRB) is one of the principal tributaries of the Tocantins River. With an area of approximately 41,500 km\u0026sup2;, it encompasses eleven municipalities in the Brazilian state of Par\u0026aacute; and has 39 tributaries. The Itacai\u0026uacute;nas is classified as a fifth-order river, with its headwaters being formed by the confluence of the \u0026Aacute;gua Preta and Azul rivers in the Serra da Seringa (located in the municipality of \u0026Aacute;gua Azul do Norte, in Par\u0026aacute; State), and its mouth is located on the left margin of the Tocantins River, in the municipality of Marab\u0026aacute;, in Par\u0026aacute; \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBetween the 1970s and 2010, approximately 50% of the IRB area was deforested, due primarily to the expansion of farming and ranching, and mining \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. About one third of the basin lies within legally protected areas, including both conservation units and indigenous lands. The region also has open-pit mines and is considered a major mineral province with considerable long-term potential for the mining of ores, which further highlights its ecological importance and need for the conservation of the associated ecosystems \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The total production of ore and other goods in the IRB account for 25% of the Gross Domestic Product (GDP) of the Brazilian state of Par\u0026aacute; \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Tapirap\u0026eacute; River, a major tributary of the Itacai\u0026uacute;nas, is not located in the vicinity of the mining complex, and its basin is adjacent to a conservation unit \u0026ndash; the Tapirap\u0026eacute; Biological Reserve (REBIO Tapirap\u0026eacute;) \u0026ndash; which has an area of 103,000 ha and provides full protection for the local fauna and flora. Most of this reserve is located within the municipality of Marab\u0026aacute; \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe \u0026Aacute;guas Claras stream is located in the central nucleus of the Caraj\u0026aacute;s National Forest, where a pilot plant was established to test ore processing methods for the Igarap\u0026eacute; Bahia gold (Au) mining project. This site consists of a small open-pit mine and a pile of rock waste.\u003c/p\u003e\u003cp\u003eThe Azul River, another tributary of the Itacai\u0026uacute;nas River, has been facing significant environmental threats from illegal gold mining, which has prompted debate among the environmental authorities \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Cinzento Stream, also known as the Cinzento River, is located in the Parauapebas basin, specifically within the Cinzento Shear Zone, which is part of the Caraj\u0026aacute;s Mineral Province. The Cinzento flows through mineral-rich areas containing ores such as Fe, Cu, and Au, and is part of the network of streams and rivers located within the IRB \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Parauapebas River Basin (PRB) has an estimated total area of 9,604.42 km\u0026sup2;, encompassing six municipalities. It contains a mineral-rich portion of the Caraj\u0026aacute;s Mineral Province, where major mining projects are located, such as the Sossego Mine (Cu), S11D (Fe), and Serra Leste (Au). This basin is also the primary source of the water supplied to almost the entire municipality of Parauapebas \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Gelado Creek is a tributary of the Parauapebas and is approximately 60 km long. It is delimited to the southeast by the Caraj\u0026aacute;s Railroad, to the northeast by the Momba\u0026ccedil;a and Gelado creeks, to the northwest by the Azul and Itacai\u0026uacute;nas rivers, and to the south by the Caraj\u0026aacute;s National Forest. This creek lies within the Igarap\u0026eacute; Gelado Environmental Protection Area, a sustainable-use conservation unit that includes both public and private lands. This stream is located in the vicinity of major mining operations, and falls within the boundaries of the Creek Gelado Environmental Protection Area, APA Igarap\u0026eacute; Gelado \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe Sossego Mine is located in the municipality of Cana\u0026atilde; dos Caraj\u0026aacute;s, near the Parauapebas River, and has been in operation since 2004. This site includes the Sossego (Cu) and S11D (Fe) projects, the latter being considered to be one of the largest iron ore mines in the world \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBiological sampling\u003c/h3\u003e\n\u003cp\u003eThe samples were collected from both altered (impacted) and preserved (control) areas during the rainy (May) and dry (November) seasons, with each campaign lasting 15 consecutive days. Sampling sites were selected based on specific criteria of the degree of anthropogenic disturbance caused by both legal and illegal mining activities, as well as their proximity to urban areas and outlets of sewage discharge.\u003c/p\u003e\u003cp\u003eAreas adjacent to mining operations were considered to be altered (impacted), while areas isolated from mining activity were classified as preserved (control areas). Four sampling sites were selected in the IRB and four in the PRB. Two control (blue) and two impacted sites (red) were selected in each basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table-S1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFish were captured using gillnets of varying mesh sizes (90 mm, 50 mm, 40 mm, and 35 mm), arranged from the most selective (largest mesh) to the least selective in the direction of the current, and left in the water for 12 hours (from 6:00 p.m. to 6:00 a.m.). Once retrieved, the fish were placed in labeled plastic bags and stored in ice-filled thermal boxes. In the laboratory, the specimens were identified to the lowest possible taxonomic level \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, and subsequently measured (total length, in mm) and weighed (total weight, in g).\u003c/p\u003e\u003cp\u003eThe physicochemical parameters of the water (pH, conductivity, temperature, and salinity) were recorded twice (at 6:00 p.m. and 6:00 a.m.) at each sampling site using a multiparameter probe (HANNA HI 98194), during the biological sampling. Samplings were authorized by the Chico Mendes Institute for Biodiversity Conservation, SISBIO license no 75796-4 and approved by the Ethics Committee on the Use of Animals at the Federal University of Par\u0026aacute; (CEUA/UFPA), protocol no. 8587260821.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll the analyses were run in the R software (R Core Team, 2023) \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Prior to analysis, the data were examines to verify the presence of outliers, the homogeneity of variance, and the normality of the residuals, as well as the potential linearity and collinearity among the variables, following the protocol proposed by Zuur et al. (2010) \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The environmental variables (conductivity, salinity, and temperature) were compared spatiotemporally (season: dry and rainy; area: impacted and control; basin: IRB and PRB), as well as in terms of their interaction with the basins, using PERMANOVA. These environmental interactions were then visualized using a Principal Components Analysis (PCA). The abiotic data matrix was standardized prior to this analysis using the Z-score method, due to the different measuring units and scales of the variables analyzed \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Collinearity was assessed using the Variance Inflation Factor (VIF), and all the variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;3 (e.g., pH) were removed, following the approach proposed by Zuur et al. (2007, 2010) \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrior to these analyses, a species accumulation curve was constructed to assess the sampling efficiency of the study. The total abundance and alpha diversity (α; measured by species richness) of the fish assemblage were analyzed separately using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), with negative binomial type I and II distributions, respectively. GAMLSS are semiparametric models in which the distribution of the response variables (continuous or discrete) may be asymmetric, and are commonly used to analyze complex data \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe distribution of the response variables was evaluated individually using the \u0026ldquo;fitDist\u0026rdquo; function of the \u0026ldquo;gamlss\u0026rdquo; package, based on the Generalized Akaike Information Criterion \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The \u0026ldquo;stepGAICAll.A\u0026rdquo; function was then used to build each model, starting from the intercept and then selecting variables step-by-step, based on the reduction of the GAIC. The final model retained only the variables that had a significant influence on the response variable.\u003c/p\u003e\u003cp\u003eThe composition and β diversity (based on the Whittaker method) of the fish assemblage were analyzed individually in relation to spatiotemporal factors (season, area, basin, and their interactions) using PERMANOVA, which was adjusted by the Bonferroni correction, through the \"adonis2\" function of the \"vegan\" package \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Here, the matrix of biotic data was standardized using the Hellinger method, as recommended by Legendre and Legendre (2012) \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The relationships among the variables were then visualized using a Principal Components Analysis (PCA). Beta diversity was calculated using the \"betadiver\" function of the \"vegan\" package [68] based on Whittaker\u0026rsquo;s method (βw), as proposed by Kollef et al. (2003) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. According to these authors, this method broadly captures the assemblage turnover, which reflects species replacement and results in high values when there are marked differences in the composition of the species.\u003c/p\u003e\u003cp\u003eThe indicator species were identified using the \"IndVal\" function of the \"labdsv\" package \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e for each spatiotemporal factor (season, area, basin, and their interactions). This analysis was based on the IndVal index, which combines the relative abundance of the species with their frequency across the samples, and ranges from 0 (no association) to 1 (exclusive occurrence of a species in each factor) \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe SCBD was also evaluated spatiotemporally (season, area, and basin), using the \"beta.div\" function of the vegan package \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. For this, the biotic matrix was previously standardized using the Hellinger method, and only comparisons within each factor resulting in differences greater than |0.03| were considered relevant.\u003c/p\u003e\u003cp\u003eFinally, a Canonical Redundancy Analysis (RDA) was run to assess the influence of the abiotic matrix on the biotic matrix. The significance of the model and its axes was determined using the \u0026ldquo;permutest\u0026rdquo; and \u0026ldquo;anova\u0026rdquo; functions, respectively, and the significance of the relationships between the environmental and biotic variables extracted from the RDA was determined using the \"envfit\" function. The \"adonis2\", \u0026ldquo;permutest\u0026rdquo;, \"anova\", and \u0026ldquo;envfit\" functions were all run with 9,999 permutations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental Characteristics\u003c/h2\u003e\u003cp\u003eWater temperature, conductivity, and salinity were all significantly higher in the dry season (PERMANOVA: F\u0026thinsp;=\u0026thinsp;9.10; p\u0026thinsp;=\u0026thinsp;0.0005; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The highest temperatures were recorded in the impacted areas, while the highest salinity was recorded in the control areas (PERMANOVA; F\u0026thinsp;=\u0026thinsp;3.51; p\u0026thinsp;=\u0026thinsp;0.0243; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The lowest mean values for all these parameters were recorded in the IRB (PERMANOVA; F\u0026thinsp;=\u0026thinsp;39.40; p\u0026thinsp;=\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the PRB, the highest mean temperature average was recorded in the rainy season, while in the highest conductivity and salinity were recorded during the dry season (PERMANOVA; F\u0026thinsp;=\u0026thinsp;4.60; p\u0026thinsp;=\u0026thinsp;0.0091; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). While the highest temperatures were recorded in impacted areas in the PRB, the lowest mean salinity and conductivity were recorded in the control areas (PERMANOVA; F\u0026thinsp;=\u0026thinsp;6.57; p\u0026thinsp;=\u0026thinsp;0.0018; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eComposition and abundance of the fish assemblage\u003c/h2\u003e\u003cp\u003eA total of 766 fish specimens were collected during the present study, representing two classes, nine orders, 22 families, and 59 species across all sites during the two seasons (see Supplementary-Table S2). The order Characiformes was the most diverse, with nine families and 14 species, followed by the Siluriformes with four families and 15 species, and the Cichliformes with one family and 10 species. The most abundant species were \u003cem\u003eSatanoperca jurupari\u003c/em\u003e (Heckel, 1840), with 101 individuals; \u003cem\u003eAgeneiosus inermis\u003c/em\u003e (Linnaeus, 1766) and \u003cem\u003eEigenmannia sp\u003c/em\u003e. (Jordan and Evermann, 1896), both with 69 individuals; \u003cem\u003eLeporinus friderici\u003c/em\u003e (Bloch, 1974), with 60 individuals; and \u003cem\u003ePlagioscion squamosissimus\u003c/em\u003e (Heckel, 1840) and \u003cem\u003eHydrolycus tatauaia\u003c/em\u003e (Toledo-Piza, Menezes \u0026amp;Santos, 1999), each with 33 individuals.\u003c/p\u003e\u003cp\u003eThe species accumulation curve demonstrated that the sampling effort was sufficient for the study assemblage, given that the composition did not vary significantly between seasons, areas, or their interactions with the basins (PERMANOVA; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b, d, e). However, the fish assemblage of the IRB basin was composed primarily of \u003cem\u003eLeporinus friderici\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;60), \u003cem\u003eHydrolycus tatauaia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;33), \u003cem\u003ePlagioscion squamosissimus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;33), and \u003cem\u003ePellona castelnaeana\u003c/em\u003e (Valenciennes, 1847) (n\u0026thinsp;=\u0026thinsp;5), while \u003cem\u003eTometes sp\u003c/em\u003e. (Valenciennes, 1850), \u003cem\u003eCichla ocellaris\u003c/em\u003e (Bloch \u0026amp; Schneider, 1801), \u003cem\u003eCynopotamus juruenae\u003c/em\u003e (Menezes, 1987), and \u003cem\u003eAuchenipterus nuchalis\u003c/em\u003e (Spix \u0026amp; Agassiz, 1829), each represented by a single specimen, were all exclusive to the PRB basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTotal fish abundance was higher during the rainy season (GAMLSS; p\u0026thinsp;=\u0026thinsp;0.0003; Supplementary Table S3; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). No significant differences were observed between the areas, basins or their interactions (GAMLSS; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Supplementary Table S3; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c, d, e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMyloplus rubripinnis\u003c/em\u003e (M\u0026uuml;ller and Troschel, 1844) (IndVal\u0026thinsp;=\u0026thinsp;0.3219), \u003cem\u003eHypostomus plecostomus\u003c/em\u003e (Linnaeus, 1758) (IndVal\u0026thinsp;=\u0026thinsp;0.3212), and \u003cem\u003eMyleus sp\u003c/em\u003e. (IndVal\u0026thinsp;=\u0026thinsp;0.2500) were identified as indicators of the dry season (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eLeporinus friderici\u003c/em\u003e was the indicator species for the IRB (IndVal\u0026thinsp;=\u0026thinsp;0.7100), although no indicator species was identified for the PRB (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the interaction between seasons and basins, only \u003cem\u003eLeporinus friderici\u003c/em\u003e (IndVal\u0026thinsp;=\u0026thinsp;0.6542), \u003cem\u003eHydrolycus tatauaia\u003c/em\u003e (IndVal\u0026thinsp;=\u0026thinsp;0.4000), \u003cem\u003ePellona castelnaeana\u003c/em\u003e (IndVal\u0026thinsp;=\u0026thinsp;0.4000), and \u003cem\u003eSerrasalmus manueli\u003c/em\u003e (Fern\u0026aacute;ndez-Y\u0026eacute;pez \u0026amp; Ram\u0026iacute;rez, 1967) (IndVal\u0026thinsp;=\u0026thinsp;0.3636) were identified as indicators of the dry season in the IRB basin. However, no species were associated with any of the other interactions (Rainy: IRB, Dry: PRB, and Rainy: PRB; Table\u0026nbsp;4). In the interaction between areas and basins, \u003cem\u003ePlagioscion squamosissimus\u003c/em\u003e (IndVal\u0026thinsp;=\u0026thinsp;0.3666) was the indicator species of the control points in the IRB basin, \u003cem\u003ePygocentrus nattereri\u003c/em\u003e (Kner, 1858) (IndVal\u0026thinsp;=\u0026thinsp;0.3499) of the control points in the PRB basin, and \u003cem\u003eSatanoperca jurupari\u003c/em\u003e (IndVal\u0026thinsp;=\u0026thinsp;0.4972) of the impacted points in the PRB basin (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No indicator species were identified for the two types of area (control vs. impacted).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndicator fish species (IndVal) identified at the study sites in the Itacai\u0026uacute;nas (IRB) and Parauapebas River basins (PRB), and their interactions with the control and impacted areas during the dry and rainy seasons. Values in bold script indicate significance differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator Species\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndVal%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMyloplus rubripinnis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHypostomus plecostomus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMyleus sp.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeporinus friderici\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLeporinus friderici\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry IRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHydrolycus tatauaia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry IRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePellona castelnaeana\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry IRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSerrasalmus manueli\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDry IRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePlagioscion squamosissimus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl IRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePygocentrus nattereri\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl PRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSatanoperca jurupari\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImpacted PRB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAlpha and Beta Diversity\u003c/h3\u003e\n\u003cp\u003eAlpha diversity (α), as measured by species richness, was significantly higher during the rainy season (GAMLSS; p\u0026thinsp;=\u0026thinsp;0.0019; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). However, no significant variation was observed between areas, basins, or their interactions (GAMLSS; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, c, d, e). In fact, there was little variation between seasons, areas or their interactions (PERMANOVA; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Supplementary Table S4; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b, d, e), although higher values of β diversity were observed in the PRB in comparison with the IRB (PERMANOVA; F\u0026thinsp;=\u0026thinsp;2.163; p\u0026thinsp;=\u0026thinsp;0.0112; Supplementary Table S4; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariation in the alpha diversity (α) of the fish species between seasons (dry and rainy-A), areas (control and impacted -B), basins (IRB and PRB-C), seasons and basins (dry:IRB; dry:PRB; rainy-IRB; rainy-PRB-D), areas and basins (impacted:IRB; impacted:PRB; reference:IRB; reference:PRB-E) in the Caraj\u0026aacute;s Mineral Province, Brazil, in May 2022, and November 2022. *indicates a significant difference among the factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eUnivariate model: Alfa diversity\u0026thinsp;~\u0026thinsp;1, sigma.formula\u0026thinsp;=\u0026thinsp;~\u0026thinsp;Season; Family\u0026thinsp;=\u0026thinsp;Binomial Negative Type II; AIC\u0026thinsp;=\u0026thinsp;224,802; R\u0026sup2;=22,44%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMu coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.08e-12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigma coefficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpecies Contributions to Beta (β) Diversity (SCBD) in the fish assemblage between rainy and dry seasons, control and impacted areas, and the IRB and PRB basins in the Caraj\u0026aacute;s Mineral Province, Brazil, in May and November 2022. Values in bold script indicate a significant contribution to the SCBD, and the asterisks (*) denote the highest values in each comparison.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAreas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eBasin\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRainy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImpacted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIRB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePRB\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEigenmannia sp.\u003c/em\u003e\u003cb\u003e0.1320*0.0473\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eSatanoperca jurupari\u003c/em\u003e\u003cb\u003e0.02280.0870*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.05590.1652*\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.00530.1034*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.1700*0.0446\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.00000.0818*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the analysis of the Species Contribution to β Diversity (SCBD) of the fish assemblages (Supplementary-Table S5), \u003cem\u003eEigenmannia sp.\u003c/em\u003e presented the highest values during the rainy season (0.1320), in impacted areas (0.1652), and in the IRB (0.1700), whereas \u003cem\u003eSatanoperca jurupari\u003c/em\u003e presented the greatest contribution in the dry season (0.0870), in impacted areas (0.1034), and in the PRB (0.0818) (Table\u0026nbsp;3).\u003c/p\u003e\n\u003ch3\u003eRelationship between fish species abundance and environmental variables\u003c/h3\u003e\n\u003cp\u003eThe water temperature, conductivity, and salinity were all related significantly to the abundance of fish species, although only the first RDA axis was significant, explaining 46.32% of the variability in the data (F\u0026thinsp;=\u0026thinsp;2.16; p\u0026thinsp;=\u0026thinsp;0.03; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eCrenicichla ocutirostris\u003c/em\u003e (G\u0026uuml;nther, 1862), \u003cem\u003eProchilodus nigricans\u003c/em\u003e (Spix \u0026amp; Agassiz, 1829), \u003cem\u003ePygocentrus nattereri\u003c/em\u003e, and \u003cem\u003ePimelodus sp.\u003c/em\u003e (Lac\u0026eacute;p\u0026egrave;de, 1803) all presented a positive relationship with salinity, conductivity, and temperature. By contrast, \u003cem\u003eLeporinus friderici, Plagioscion squamosissimus, Eigenmannia sp., Geophagus neambi\u003c/em\u003e (Lucena \u0026amp; Assis, 2010), \u003cem\u003eRetroculus lapidifer\u003c/em\u003e (Castelnau, 1855), and \u003cem\u003eSatanoperca jurupari\u003c/em\u003e were associated negatively with these variables.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhile no significant variation in abundance was found between basins, fish were clearly more abundant in the PRB basin during the rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePhenotypic plasticity\u003c/h2\u003e\u003cp\u003eThe phenotypic plasticity of the fish assemblage studied here may account for the considerable similarities in total abundance, species composition, and taxonomic diversity found between impacted and control areas, and between the two study basins. This mechanism is defined as the capacity of a genotype to exhibit varying responses to shifts in the environment \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. In aquatic environments, this plasticity is expressed through physiological, morphological, and behavioral adjustments, including changes in the tolerance of the species to reduced water quality, its habitat use, diet, and bodily proportions \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA number of different studies have shown that tropical fish have a considerable potential for physiological adjustment in response to environmental stressors such as changes in salinity, temperature, pH, and oxygen availability, which may help to explain, in part, their persistence in environments impacted by mining \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. In particular, increased exposure to metals and high conductivity, conditions that are typical of areas affected by mining, may trigger compensatory physiological mechanisms. Tolerance of salinity, for example, has been associated with the plasticity of ion transport mechanisms in Neotropical fish, which can contribute to their survival in degraded environments \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese traits increase the likelihood of survival for the more tolerant species, allowing them to persist even under extremely altered environmental conditions \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. However, while phenotypic plasticity may underpin the capacity of communities to persist in the short to medium term, it should not be interpreted as a sign of ecological stability, given that it may mask losses in functional diversity and long-term resilience \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e,85\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn fact, threshold analyses of the tolerance of fish in watersheds in the eastern United States revealed significant negative effects of mining on the diversity and evenness of the fish assemblages, indicating that the mines act as regional sources of disturbance \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Similarly, although the evidence from the present study indicates some resilience in the fish populations exposed to impacts from mining, the accumulated evidence indicates that, in regions with intense and persistent mining operations, the impacts on the local aquatic communities are both significant and multifaceted.\u003c/p\u003e\u003cp\u003eIn Wabush Lake (Canada), iron (Fe) tailings caused a vitamin A deficiency in lake trout (\u003cem\u003eSalvelinus namaycush\u003c/em\u003e), which led to skin whitening syndrome \u003csup\u003e87\u003c/sup\u003e. In Brazil, fish exposed to water contaminated by the Fund\u0026atilde;o dam tailings (in Mariana, Minas Gerais state) presented moderate to severe histological damage of the gonads, which may compromise reproductive processes over the long term \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Significant histopathological alterations were also observed in the gills of \u003cem\u003eAstyanax aff. bimaculatus\u003c/em\u003e exposed to high concentrations of zinc, Zn \u003csup\u003e89\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe results of the present study indicate that tolerance mechanisms, such as phenotypic plasticity, may play a central role in the apparent resilience of the fish assemblage of Caraj\u0026aacute;s, a region that has been impacted profoundly by large-scale mining operations for more than three decades. However, this may mask deeper and progressive changes in the structure of the local fish communities, especially over the long term, which reinforces the need for further, continuous monitoring, including the assessment of functional diversity, the physiological health of the organisms, and environmental quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eTemporal Patterns\u003c/h2\u003e\u003cp\u003eThe greater abundance and taxonomic diversity observed during the rainy season, together with the lack of indicator species exclusive to this period, indicate that the species that make up the local assemblage are mostly opportunistic generalists. These species can benefit from the temporary increase in the availability of food and shelter during this period, without necessarily establishing patterns of ecological exclusivity. In fact, many of the species recorded during the rainy season also occur in the dry season, albeit at a lower abundance, reflecting survival strategies that encompass varying environmental conditions.\u003c/p\u003e\u003cp\u003eAlthough no indicator species were detected during the rainy season, the environmental conditions during this period favor the entry of individuals of many different species, increasing the total number of individuals without any single species reaching the threshold considered to be typical of an indicator species. As river levels rise during the rainy season, aquatic habitats expand and the connectivity between environments increases, facilitating the entry of many species into the floodplain \u003csup\u003e90\u003c/sup\u003e. This increased connectivity may also promote homogenization of the community and thus reduce species dominance, making it more difficult to detect any indicator species \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSimilar patterns have been observed in tropical streams \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e94\u003c/span\u003e,95\u003c/sup\u003e and in the Esa-Odo Reservoir in Spain \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. However, very different patterns have also been reported in studies in other areas, including streams in Central Amazonia \u003csup\u003e97\u003c/sup\u003e, the Piaga\u0026ccedil;u-Purus Sustainable Development Reserve on the Purus River in the Brazilian state of Amazonas \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e, and in lakes in Nigeria \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e, where higher fish abundance was observed during the ebb season, in contrast with the findings of the present study. In Manaus (Amazonas state, Brazil), Esp\u0026iacute;rito-Santo et al. (2009) \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e found no significant variation in fish abundance between seasons in protected areas.\u003c/p\u003e\u003cp\u003eThe dominance of \u003cem\u003eM. rubripinnis, H. plecostomus\u003c/em\u003e, \u003cem\u003eL. friderici\u003c/em\u003e and \u003cem\u003eMyleus sp\u003c/em\u003e. as indicator species for the dry season, appears to be associated with their physiological tolerance, interactions with abiotic parameters, feeding plasticity, and ecological adaptations, which result in distinct behavioral responses to seasonal variation. \u003cem\u003eMyloplus rubripinnis\u003c/em\u003e, \u003cem\u003eMyleus sp\u003c/em\u003e., and \u003cem\u003eL. friderici\u003c/em\u003e have herbivorous/frugivorous feeding niches, and during the dry season, these species can be found in isolated pools or channels, where they feed primarily on aquatic vegetation and the detritus that accumulates during periods of low water [101,102,103]. \u003cem\u003eHypostomus plecostomus\u003c/em\u003e is able to tolerate both high temperatures and hypoxia, and in the dry season, it is able to alter ecosystem structure through the reduction of the periphyton and the promotion of the cycling of nutrients, such as phosphorus (P). At high densities, then, these species may all act as nutrient sinks \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e104\u003c/span\u003e,105\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe identification of \u003cem\u003eP. squamosissimus\u003c/em\u003e as an indicator specie for the control area in the IRB indicates that, while it is able to tolerate variation in the environment, it tends to be more successful in less disturbed environments. The feeding habits and reproductive behavior of this species make it a valuable bioindicator of environmental quality. As a carnivore, which feeds primarily on crustaceans and small fish, \u003cem\u003eP. squamosissimus\u003c/em\u003e is commonly found in environments where these resources are abundant \u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMoreover, \u003cem\u003eP. squamosissimus\u003c/em\u003e presents reproductive habits that are synchronized with the hydrological regime, relying on seasonal flooding to guarantee its reproductive cycle, including the recruitment of juveniles, in addition to hydrologically intact systems with preserved areas of floodplain \u003csup\u003e108,\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e,\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e. While \u003cem\u003eP. squamosissimus\u003c/em\u003e presents some degree of phenotypic plasticity \u003csup\u003e111\u003c/sup\u003e, then, there are ecological limits to its persistence in degraded environments. Rocha et al. (2016) \u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e investigated the cytotoxicity and mutagenicity of the waters of the Maraj\u0026oacute; archipelago (Par\u0026aacute;, Brazil) using \u003cem\u003eP. squamosissimus\u003c/em\u003e as a bioindicator, and found that exposure to pollutants can induce genetic alterations in this species. Oliveira et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e also reported genotoxic effects in \u003cem\u003eP. squamosissimus\u003c/em\u003e in Amazon estuaries impacted by mining, using this species as a biomonitor, and observed that the fish examined were influenced directly by xenobiotic agents, which also impacted their genetic material.\u003c/p\u003e\u003cp\u003eSpecies that appear exclusively or preferably during the dry season tend to seek out more stable and isolated environments that create well-defined habitats, in which certain species are confined and become rare or absent during the rainy season, which can result in significant IndVal values.\u003c/p\u003e\u003cp\u003eThe identification of \u003cem\u003ePygocentrus nattereri\u003c/em\u003e as an indicator species for the control areas in the PHRB basin may be related to its greater abundance in areas with reduced forest cover. These areas generally provide readier access to feeding resources, which is essential for the growth and reproduction of the species \u003csup\u003e114\u003c/sup\u003e. This pattern reflects the tolerance of these species of structurally simpler and more open environments, which is a common trait in the generalist species that are able to exploit a variety of niches and persist in areas subject to moderate anthropogenic disturbance \u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e,\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e,\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, \u003cem\u003eP. nattereri\u003c/em\u003e may benefit from relatively stable and preserved ecological conditions, given that less impacted environments tend to have a greater availability of prey, better reproductive conditions and adequate shelter, all of which favor the ecological performance of the species \u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e,\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e. It is also well established that generalist species tend to have greater eurytopy in comparison with more specialist species \u003csup\u003e\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe high water temperatures recorded during the rainy season in the PRB were influenced by the controlled release of surface water by the mining company at one of the sampling points. In fact, the increased availability of food and habitats during the rainy season may also favor the general persistence of fish in Caraj\u0026aacute;s.\u003c/p\u003e\u003cp\u003eIn studies of streams in the United States, Daniel et al. (2015) \u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e reported that mining has negative effects on the diversity and evenness of fish assemblages, as well as the number of taxa with specific strategies, and trophic and habitat preferences. Even so, the absence of detectable changes in the fish diversity of Caraj\u0026aacute;s, despite the duration of the present study, likely reflects a process of biotic homogenization, in which the more sensitive species are replaced by more tolerant ones, thereby maintaining species richness, while reducing functional and phylogenetic diversity, although the latter two parameters were not examined in the present study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eThe fish assemblage\u003c/h2\u003e\u003cp\u003eThe composition and stability of the fish assemblage varied between the two study basins, with the PRB having a higher species turnover (β-diversity), despite the lack of difference in total abundance or alpha diversity. No indicator species were found in the PRB, although the IRB assemblage did appear to be more stable, driven by the consistent presence and relative abundance of \u003cem\u003eL. friderici\u003c/em\u003e. Furthermore, species turnover in the PRB may have been influenced by the hydrological conditions predominating during the rainy season, with \u003cem\u003eEigenmannia sp\u003c/em\u003e. contributing most to the SCBD of the β-diversity in the impacted areas during the rainy season.\u003c/p\u003e\u003cp\u003eThe species of the order Gymnotiformes are widely used as bioindicators of environmental quality due to their specific physiological and behavioral characteristics, such as the waveform, amplitude, and repetition rate of their Electric Organ Discharges (EOD). These parameters are directly linked to the physicochemical properties of water, especially its electrical conductivity, ion concentration, and the presence of contaminants, and can be altered significantly in response to environmental stressors \u003csup\u003e122,\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e\u003c/sup\u003e. The presence of toxic elements in the water, such as metals, may affect both the production and modulation of electric signals, which can impair essential functions such as communication, orientation, and foraging, in addition to impacting the energetic metabolism of these fish.\u003c/p\u003e\u003cp\u003eThe environmental characteristics observed in the IRB basin, combined with the eco-physiological sensitivity of \u003cem\u003eEigenmannia sp\u003c/em\u003e., may explain its irregular occurrence or more frequent replacement in the impacted areas. This indicates that the species responds negatively to changes in water quality, potentially being excluded or displaced from environments under diffuse pollution or significantly altered conductivity. This species may thus represent a sensitive biological indicator of local conditions.\u003c/p\u003e\u003cp\u003eAlthough the PRB did not have any exclusive indicator species, \u003cem\u003eSatanoperca jurupari\u003c/em\u003e contributed most to species turnover during the dry season in the impacted areas. A positive relationship was also observed between the abundance of both \u003cem\u003eC. ocutirostris\u003c/em\u003e and \u003cem\u003eP. nigricans\u003c/em\u003e with water temperature, which indicates that these species have physiological adaptations to warmer environments, such as an elevated basal metabolism.\u003c/p\u003e\u003cp\u003eIn ectothermic fish, such as teleosts, the ambient temperature regulates directly the metabolic rate, that is, as the temperature increases, so does the demand for energy, leading to a higher respiratory rate, oxygen consumption, and excretion of ammonia (NH₃), which is the primary byproduct of protein metabolism \u003csup\u003e124\u003c/sup\u003e. Ammonia is excreted primarily through the gills, via passive diffusion and active transport, and excessive production of this compound can disrupt the acid-base balance of the organism, which requires complex physiological adjustments, especially in warm environments with reduced oxygen concentrations \u003csup\u003e\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e,\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe presence of \u003cem\u003eP. nigricans\u003c/em\u003e and \u003cem\u003eC. ocellata\u003c/em\u003e in warmer locations, as observed in the present study, may reflect their capacity to withstand these increased physiological demands, which favors their persistence in areas with higher surface water temperatures. As highlighted by Azzurro (2008) \u003csup\u003e\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e127\u003c/span\u003e\u003c/sup\u003e, species with amplified thermal tolerance tend to expand into or dominate warmer environments, which are often associated with anthropogenic disturbance, thereby promoting alterations in the structure of aquatic communities.\u003c/p\u003e\u003cp\u003eThe positive relationship between \u003cem\u003eP. nigricans\u003c/em\u003e and salinity indicates a possible adaptation associated with its migratory habits, which is a common pattern in the Neotropical fish that move between environments that vary in their salt concentrations \u003csup\u003e128,\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e129\u003c/span\u003e,130,\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e. In Amazonian environments, the fluctuation in water levels over the course of the hydrological cycle leads to significant variation in the physicochemical parameters of the water, including its dissolved salt concentration, which demands considerable ecological and physiological flexibility from the resident fish.\u003c/p\u003e\u003cp\u003eIn the context of mining in the Caraj\u0026aacute;s region, the localized increase in salinity may be related to mineral leaching and alterations in the hydrological regime provoked by mining activities, which can contribute to an increase in the electrical conductivity of the water, which is often used as an indirect indicator of dissolved ion (salt) concentrations. Given this, the presence of P. nigricans in areas with higher salinity may reflect both the physiological tolerance of this species and its adaptive behavioral response to disturbed environments.\u003c/p\u003e\u003cp\u003eThe observed relationship between \u003cem\u003ePygocentrus nattereri\u003c/em\u003e and conductivity indicates that this species is tolerant to environmental stressors, given that elevated conductivity reflects the presence of pollutants or dissolved solids in the water \u003csup\u003e\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u003c/sup\u003e. This tolerance reflects the ability of \u003cem\u003eP. nattereri\u003c/em\u003e to persist in environments with significant chemical and physical variations, which are common in areas impacted by activities such as mining. However, while this plasticity allows for the survival of the species, continuous exposure to these stressors may have a negative effect on the stability of the population and, in turn, that of the aquatic community as a whole.\u003c/p\u003e\u003cp\u003eClearly, the continuous monitoring of the physiological and population responses, especially of the indicator species identified in the present study, is fundamental to the understanding of the cumulative impacts of environmental pressures and, in the specific case of Caraj\u0026aacute;s, one potential measure would be too make mining companies responsible for providing specimens periodically for analysis, based on a systematic sampling protocol. These analyses should be funded by the mining companies, but conducted by universities, as a condition for the licensing of mining operations. An approach of this type, in addition to establishing more effective programs for the control of contamination, would greatly enhance long-term data cataloging, the training of personnel, and the optimization of analytical methods.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e\u003cp\u003eNone of the authors declares any conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003cp\u003eThe study did not involve animal experimentation in the laboratory. All fish analyzed were collected exclusively from the natural environment, and all procedures were conducted under the appropriate ethical and legal authorizations, by the Chico Mendes Institute for Biodiversity Conservation, SISBio license no 75796-4 (see supplementary S6) and approved by the Ethics Committee on the Use of Animals at the Federal University of Par\u0026aacute; (CEUA/UFPA), protocol no. 8587260821 (see supplementary S7).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003cp\u003eAll the authors declare consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eAll the authors declare consent.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eResearch funded by Cooperation Agreement No. 04/2018 between ICMBio, Salobo Metais S.A and Funtec-DF. This research is mainly supported by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES, Brazil), through the Postgraduate Program in Aquatic Ecology and Fisheries (PPGEAP), Center for Aquatic Ecology and Fisheries of the Amazon (NEAP), Federal University of Par\u0026aacute; (UFPA, Brazil).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCACRO: Project administration, collection of samples in the field, Writing\u0026nbsp;- Original draft, Methods, Investigation, Formal analysis, Data curation, Formal analysis; KSM: Writing \u0026ndash; Formal analysis; Assistance in writing and reviewing the manuscript, Data curation; JPSO: Writing- Assistance in writing and reviewing the manuscript; AOM: Assistance in writing and reviewing the manuscript, Statistical analysis of the data; ABFS: Writing\u0026nbsp;- Assistance in writing and reviewing the manuscript; DCP: Collection of samples in the field; BB: Collection of samples in the field, Supervision, Review \u0026amp;amp; editing, Funding acquisition, Conceptualization.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the Chico Mendes Institute for Biodiversity Conservation (ICMBIO) for funding the project that generated the data for this study and for all the logistical support during the collection campaigns in the Caraj\u0026aacute;s Mosaic. Author CACRO would like to thank the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil) for its support (Phd scholarship - Proc 495 88887.082622/2024-00).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChecon, H. H., Costa, H. H. R., Corte, G. N., Souza, F. M. \u0026amp; Pombo, M. Rainfall Influences the Patterns of Diversity and Species Distribution in Sandy Beaches of the Amazon Coast. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 5417 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorrea, S. B. et al. Floodplain forests drive fruit-eating fish diversity at the Amazon Basin-scale. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 122, (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarcia, A. et al. Biodiversity hotspots and threatened species under human influence in the Amazon continental shelf. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTickner, D. et al. Bending the Curve of Global Freshwater Biodiversity Loss: An Emergency Recovery Plan. \u003cem\u003eBioScience\u003c/em\u003e 70, 330\u0026ndash;342 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMani\u0026ccedil;oba, R. S. Urbaniza\u0026ccedil;\u0026atilde;o e qualidade de vida nos munic\u0026iacute;pios da Amaz\u0026ocirc;nia Legal criados ap\u0026oacute;s 1988. 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Modeling pH changes and electrical conductivity in surface water as a result of mining activities. \u003cem\u003eNaukovij v\u0026igrave;snik Nac\u0026igrave;onalʹnogo g\u0026igrave;rničogo un\u0026igrave;versitetu\u003c/em\u003e. 122\u0026ndash;129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33271/nvngu/2024-1/122\u003c/span\u003e\u003cspan address=\"10.33271/nvngu/2024-1/122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaptions.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fish assemblage, Impacts of mining, Amazon Basin, Biodiversity indicators, Alpha and beta diversity, Phenotypic plasticity, Environmental monitoring","lastPublishedDoi":"10.21203/rs.3.rs-7887845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7887845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe impacts of mining on aquatic biodiversity pose significant challenges for environmental mitigation, particularly near protected areas. This study assessed fish abundance and diversity (assemblage composition, alpha and beta diversity, indicator species, and Species Contribution to Beta Diversity \u0026ndash; SCBD) in the Itacai\u0026uacute;nas and Parauapebas river basins, Brazilian Amazonia, across dry and rainy seasons. We hypothesized that basins near mining-impacted areas would exhibit reduced fish abundance and diversity compared to control areas. To test this, four sampling points were established per basin (two controls, two impacted), totaling eight sites, where fish were collected using gillnets and physicochemical water parameters were simultaneously recorded. Fish abundance and alpha diversity peaked during the rainy season but showed no significant differences between control and impacted areas. However, beta diversity was highest in the Parauapebas basin, and distinct indicator species emerged: \u003cem\u003ePlagioscion squamosissimus\u003c/em\u003e and \u003cem\u003ePygocentrus nattereri\u003c/em\u003e were associated with control areas, while \u003cem\u003eSatanoperca jurupari\u003c/em\u003e characterized impacted sites. A Redundancy Analysis (RDA) linked environmental variables (temperature, salinity, conductivity) to species distributions, reflecting water quality influence. Our data suggest that phenotypic plasticity might mask mining's negative effects by favoring generalist species. While abundance and richness did not clearly differentiate areas, beta diversity patterns and indicator species highlight the critical need for continuous, integrated monitoring to assess long-term ecological shifts.\u003c/p\u003e","manuscriptTitle":"The use of fish diversity and abundance as environmental indicators in a mining region in Brazilian Amazonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 13:02:50","doi":"10.21203/rs.3.rs-7887845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T13:55:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T13:50:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T19:22:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216400881258363074408463624981969622674","date":"2026-01-27T00:22:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184897356405984294823556329049208665665","date":"2026-01-26T14:00:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91036327611749317374859765556164001143","date":"2026-01-22T19:02:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131703382583029902304464898308078895611","date":"2025-11-26T13:38:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167520320720276824207864637615336520162","date":"2025-11-06T01:41:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179176943940595410328571081530858114874","date":"2025-10-29T12:47:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-28T20:08:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-28T20:01:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-28T17:26:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-27T03:47:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-27T03:24:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58d5851b-b447-4aa0-a3b5-b140333ffa82","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57517628,"name":"Biological sciences/Ecology"},{"id":57517629,"name":"Earth and environmental sciences/Ecology"},{"id":57517630,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-03-16T16:18:09+00:00","versionOfRecord":{"articleIdentity":"rs-7887845","link":"https://doi.org/10.1038/s41598-026-43889-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-12 15:59:14","publishedOnDateReadable":"March 12th, 2026"},"versionCreatedAt":"2025-11-07 13:02:50","video":"","vorDoi":"10.1038/s41598-026-43889-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43889-8","workflowStages":[]},"version":"v1","identity":"rs-7887845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7887845","identity":"rs-7887845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}