The ichthyofaunal composition of Amazonian streams reflects localized environmental changes associated with buried mining pipeline infrastructure

Izabella Cristina da Silva Penha^1,2 , Naraiana Loureiro Benone³, Ana Júlia Pinheiro da Silva^1,2, Ronaldo Souza da Silva¹, Thaisa Sala Michelan^2,4, Leandro Juen^1,2 and Luciano Fogaça de Assis Montag^1,2

PDF: EN    XML: EN | Supplementary: S1 S2 S3 S4 S5 | Cite this article

Associate Editor: Carla Pavanelli

Section Editor: Fernando Pelicice

Editor-in-chief: Carla Pavanelli

Abstract​

Introduction​

Land-use changes driven by the expansion of agriculture, livestock farming, and mining have led to significant impacts on aquatic biodiversity and ecological processes in freshwater ecosystems (Ortega et al., 2021). These alterations can range from habitat loss to shifts in the composition and functioning of biological communities (Martínez-Meyer et al., 2013). The structure of aquatic communities is shaped across multiple spatial and temporal scales, influenced by both regional factors — such as climate and geology — and local factors, including water quality and habitat complexity (Poff, 1997; Richter et al., 1997; Allan, 2004). Human-induced changes, such as increased temperature, sedimentation, nutrient input, and loss of habitat connectivity, can modify environmental conditions in streams and negatively affect the richness, diversity, and abundance of aquatic organisms (Kerr, Werner, 1980; Bellard et al., 2012).

Such environmental degradation may also compromise ecosystem functioning, reducing ecological resilience and stability (Scheffer et al., 2003; Ficke et al., 2007; Zeni et al., 2019). Several studies have demonstrated that land-use impacts — including riparian vegetation removal, agricultural intensification, and urbanization — can alter the composition of fish and aquatic macroinvertebrate communities (Carvalho et al., 2013; Monteiro-Júnior et al., 2014; Cunha et al., 2015; Leal et al., 2016; Joy et al., 2019). Streams surrounded by landscapes with greater native vegetation cover tend to support a higher richness and abundance of sensitive species, while degraded areas often harbor more tolerant and generalist species (Harding et al., 1998; Iwata et al., 2003; Burcher et al., 2008).

Alterations in the structure and heterogeneity of habitats, such as streambed simplification and the loss of microhabitats, directly influence the diversity of aquatic communities (Brumbaugh et al., 2005; Garcia et al., 2023). The longitudinal connectivity of streams is also critical for the maintenance of ecological processes, being affected by physical barriers or changes in hydrological characteristics, such as damming and flow alterations (Pinto et al., 2009; Roa-Fuentes, Casatti, 2017). In this context, the implementation of buried infrastructure, such as slurry pipelines, can lead to localized environmental alterations, particularly when accompanied by surface access roads or vegetation clearance during installation. Although the pipelines themselves are underground and do not visibly impound streamflow, associated service roads may compromise streambank stability, increase sediment input, and disrupt riparian connectivity. Thus, the construction of such infrastructure represents an additional source of environmental impact on aquatic ecosystems. In addition to vegetation removal and soil disturbance during installation, slurry pipelines can cause changes in the morphology of water bodies and local hydrodynamics (Mechi, Sanches, 2010; Cooney, Kwak, 2013; Macedo, Castello, 2015; Negrão et al., 2018, 2021). Studies have indicated that the installation of slurry pipelines and associated works, such as access roads and containment basins, can alter streamflow regimes, damming sections and transforming lotic habitats into lentic ones, thereby affecting the distribution and composition of aquatic species (Casatti et al., 2006; Gomes et al., 2016).

Additionally, the presence of structures such as slurry pipelines can cause differential changes along the course of the stream, particularly between upstream and downstream sections. Damming or alteration of flow can lead to sediment accumulation, increased temperature, decreased dissolved oxygen, and changes in flow velocity in downstream sections, affecting the availability of microhabitats and promoting the exclusion of species more sensitive to physical-chemical changes in the water (Lévesque, Dubé, 2007; Leitão et al., 2017). These changes can lead to the simplification of trophic structure, reduced functional diversity, and impairment of the ecosystem services provided by streams (Casatti et al., 2006; Monteiro-Júnior et al., 2014).

This study aims to evaluate the impacts of land use and land cover on the structure of ichthyofauna, focusing on how local environmental and local-scale land use variables influence fish richness and composition. Specifically, we address three questions: (i) will the richness and composition of the ichthyofauna be influenced by the structural and environmental changes in the streams under the influence of the structure created for the use of the mining pipeline? (ii) Will there be any difference in the species composition found in impacted streams compared to control streams? (iii) Are there differences in the composition of the ichthyofauna when comparing upstream and downstream stretches of the sampled streams? We hypothesize that changes in the structure of habitat found in streams subjected to different types of impact will negatively influence fish diversity. We expect preserved streams to exhibit higher taxonomic diversity than streams under direct or indirect impacts, due to reduced ecological complexity in impacted streams. Furthermore, we predict that the river distance between all sampled streams will be a key determinant of species composition and richness. Streams located in close proximity, particularly those in preserved areas, should experience greater species exchange and diversity, while fragmented streams near the pipeline, with reduced connectivity, should experience lower diversity and homogenization of fish assemblages.

Material and methods

Study area. The study was conducted in 53 streams, 20 streams had a buried mining pipeline directly crossing their channels, while 33 streams were situated outside the direct influence area of the pipeline (Fig. 1). It is important to note that the pipeline remains entirely buried where it intersects stream channels, creating no visible surface barriers or impoundments. However, the associated aboveground access and maintenance roads may contribute to localized environmental alterations, including increased sediment input, modification of riparian zones, and disruption of streambank stability. Sampling points were chosen across different land-use types (pasture, monoculture, and human-influenced areas), forming a gradient or mosaic of land use and cover. Sampling was carried out during the dry season (the period of lowest annual precipitation) in October 2022 and October 2023.

FIGURE 1| Location of the study area near the bauxite pipeline extension in the State of Pará, Eastern Amazon, Brazil.

To ensure spatial independence among samples, we selected 1st to 3rd-order streams (Strahler, 1957) located in separate micro-basins, maintaining a minimum distance of three kilometers between sampling sites. The municipality of Paragominas, where bauxite mining operations are established, serves as the industrial hub, situated 64 km from the urban center. The drainage network is characterized by the Capim and Gurupi rivers, which feature U-shaped valleys, streams, and extensive floodplains (Cruz, 2011). The region’s climate is classified as Am under the Köppen classification, corresponding to a typical equatorial tropical pattern with rainfall distributed throughout the year. The rainy season occurs predominantly from December to May, while the dry season extends from June to November.

Data collection. Ichthyofauna. In each stream, a 150-meter section was subdivided into equidistant transverse transects labeled “A” to “K” upstream, resulting in 10 longitudinal sections of 15 m each. In streams influenced by the pipeline, the pipeline intersected section “F” at its midpoint, with 75 m upstream and 75 m downstream of the pipeline. This design aimed to detect impacts both upstream (related to impoundment and the transformation of a lotic environment into a semi-lentic one) and downstream (increased sediment input, higher light penetration, and elevated water temperature). Fish were sampled using metal mesh sieves with a 5 mm mesh size. Each section was subjected to a standardized sampling effort of six hours of sieving, with time divided between segments and collectors. This method has been successfully applied in the Eastern Amazon (Prudente et al., 2017; Benone et al., 2018).

Collected fish were fixed in 10% formalin solution and, after 48 h, transferred to 70% ethanol for subsequent identification at the Laboratory of Ecology and Conservation of the Biological Sciences Institute, Universidade Federal do Pará. Identification of specimens was carried out to the lowest possible taxonomic level using identification keys (Kullander, 1986; Britski et al., 2007) and assistance from specialists when necessary. All specimens were deposited in the Ichthyological Collection of the Universidade Federal do Pará, Belém, Pará, Brazil, and are available for verification or consultation.

Local environmental variables. To assess potential environmental changes caused by the bauxite pipeline, we applied a protocol comprising 270 metrics that evaluate the physical integrity of streams (Tab. S1). These metrics are grouped into seven distinct blocks: land cover and use, human influence, substrate type, flow, wood disposition, and channel morphology (Kaufmann et al., 1999; Peck et al., 2005; Callisto et al., 2014). Additionally, we applied the Habitat Integrity Index (HII) developed by Nessimian et al. (2008), which is calculated based on 12 variables, yielding a score ranging from 0 to 1. These variables include riparian forest width and integrity, land-use patterns beyond the riparian zone, stream substrate type, aquatic vegetation, among others.

Simultaneously, physical and chemical variables of the streams were measured at three equidistant points within each section, including pH, dissolved oxygen (mg/L), electrical conductivity (mS/cm), and temperature (°C). These parameters were measured using a portable multiparameter probe (AKSO/SX839). To evaluate stream complexity, we applied the Habitat Diversity Assessment Protocol described by Peck et al. (2005).

Based on the literature, we pre-selected 45 environmental variables, including: dissolved oxygen (Prudente et al., 2017; Brejão et al., 2021; Palheta et al., 2021); temperature (Pinto et al., 2009; Prudente et al., 2017; Roa-Fuentes, Casatti, 2017; Brejão et al., 2021); thalweg depth (Montag et al., 2019; Freitas et al., 2021; Palheta et al., 2021); wetted width; immersion + banks (Juen et al., 2016; Palheta et al., 2021); fine gravel (Palheta et al., 2021); substrate < 16 mm (Leitão et al., 2017; Roa-Fuentes, Casatti, 2017; Seabra et al., 2022); leaf litter (Santos et al., 2019; Seabra et al., 2022); organic matter and roots (Mendonça et al., 2005; Santos et al., 2019); fast flow (Benone et al., 2020); flow heterogeneity (Brejão et al., 2021; Freitas et al., 2021); canopy over the channel; woody undergrowth < 0.5 m; woody understory 0.5–5 m (Montag et al., 2019); average canopy cover (Prudente et al., 2017); in-stream wood class 1 (Benone et al., 2017).

Thus, we utilized this subset extracted from the initial 277 environmental variables (protocol metrics + physicochemical variables + HII) and selected the final variables through the following steps: we calculated the percentage of zeros and excluded all variables with more than 70% of their data consisting of zeros. To minimize issues of multicollinearity, we adjusted pairwise correlations using Pearson’s method for continuous data and Spearman’s method for ordinal data. When a high correlation (r > 0.7) was detected, we consistently selected the variable deemed most relevant to the fish communities in Amazonian streams. Finally, for continuous data only, we conducted a forward selection procedure (forward.sel) and retained only those variables with p-values < 0.05.

Local-scale land use. We calculated the percentage of land cover use for each sampled site using data on land use and land cover categories classified by the MapBiomas project (MapBiomas, 2022). MapBiomas integrates annual Sentinel-2 satellite images from 2016 to 2022 (Collection 7), classifying various land use and cover categories in Brazil on a pixel-based grid with a resolution of 10 × 10 m, utilizing a Random Forest algorithm (MapBiomas, 2022). Each site was represented by a grid of pixels where land cover was coded as 1 (presence) or 0 (absence) for each category of interest. A circular buffer with a radius of 300 m was created around the coordinates of each stream, and the number of pixels within the buffer corresponding to each land use category was summed and divided by the total buffer area. The percentage of land cover within the buffer was then calculated. The following categories were used: Forest (including Forest Formation and Savanna Formation), Agriculture (Pasture, Agriculture, and Forestry), Mining (Mining), and Exposed Soil (Urban Area and Beach, Dune, and Bare Soil), following the MapBiomas classification system.

Fluvial distance. We recorded the geographical coordinates of each stream using a GPS (Global Positioning System; model Garmin GPSMAP 65), which was recorded at the beginning of the 150-meter stretch sampled. Spatial variables were obtained by calculating the fluvial distance between streams, accounting for the direction of water flow. To perform this analysis, we applied the Asymmetric Eigenvector Maps (AEMs) technique (Blanchet et al., 2011). The axes generated by AEMs describe simple and complex spatial patterns at different scales when ordered by value. The scores of these axes can be used to understand the influence of spatial structure among streams on variations in fish assemblage composition. During the analysis, the AEM axes generated were tested using Moran’s I, with a significance level of less than 5%; only significant AEM axes were used as predictor variables in subsequent analyses (Zbinden et al., 2022).

Data analysis. In our study, each stream represented a sampling unit, totaling 53 samples. Local-scale land use variables were reduced using Principal Component Analysis (PCA) based on the values of each Local-scale land usefeature. We utilized the first axis (PC1; Tab. S3,Fig. S2), which explained the highest percentage of variance. Metrics retained by the PCA criterion were used as corresponding Local-scale land use variables (Tab. S3). Prior to analysis, data were transformed or standardized to minimize potential effects of outliers. To assess potential differences in fish species richness between Control and Pipeline categories, we performed a Student’s t-test (Zar, 1999). Normality and homogeneity assumptions were evaluated using the Shapiro-Wilk and Levene tests, respectively.

Effects of environmental variables on species richness. We used an Analysis of Covariance (ANCOVA) to evaluate the influence of environmental, spatial, and local-scale land use variables (forest cover, bare soil, agriculture and pasture, and mining) on fish species richness in the Control and Pipeline categories. Model diagnostics for residual uniformity, overdispersion, and outlier detection were conducted using the simulate residuals function from the DHARMa package (Hartig, 2022). Predictor variables were selected using a forward selection approach via the forward.sel function from the adespatial package (Dray et al., 2012). All selected variables were standardized using Z-scores prior to analysis.

Effects of variables on species composition. The influence of environmental, spatial, and local-scale land use variables on fish species composition was assessed using distance-based Redundancy Analysis (db-RDA; Legendre, Anderson, 1999). The fish composition matrix (response matrix in db-RDA) was Hellinger-transformed to reduce the influence of highly abundant species (Peres-Neto, Legendre, 2010). Predictor variables for the db-RDA were selected using the ordistep function from the vegan package, which applies both forward and backward stepwise selection. The variable “Category” (Control vs. Pipeline) was excluded from this selection. The global significance of the db-RDA was tested using ANOVA with 9,999 permutations.

We evaluated the contributions of fish species, environmental, and spatial variables to variation in species composition using the envfit procedure (Oksanen et al., 2015), with 9,999 permutations to test the significance of associations between species and environmental/spatial predictors.

Differences in species composition (using Hellinger-transformed abundance data) between categories were tested using PERMANOVA, followed by a test of multivariate dispersion (Betadisper). Both analyses used Bray-Curtis dissimilarity. The Betadisper test assessed the homogeneity of dispersions among groups in multivariate space and served as a proxy for heterogeneity. Differences were tested via ANOVA. Together, these analyses allowed us to evaluate both differences in composition and variation (e.g., polygonal area differences) among categories.

Contribution of variable sets. We conducted a partial Redundancy Analysis (pRDA) to partition the individual and shared contributions of local environmental, local-scale land use, and spatial variables to both fish species richness and composition (Borcard, Legendre, 2002). Only those local environmental variables selected by the methods described above were used. The statistical significance of variance fractions was tested using permutation tests with 9,999 iterations (McArdle, Anderson, 2001). All analyses were performed in R (R Development Core Team 2023), and a 5% significance threshold was adopted for all tests.

Upstream vs. downstream comparisons. Finally, to assess differences in fish species composition between upstream (0–75 m) and downstream (75–150 m) stream segments, we used multivariate PERMANOVA. Comparisons were performed within each category (Control and Pipeline) and between upstream and downstream segments across categories. The analysis was based on a Bray-Curtis dissimilarity matrix built from fish abundance data. Samples were grouped by position within the stream (upstream or downstream), and PERMANOVA was conducted with 999 permutations using the adonis2 function from the vegan package in R.

Results​

A total of 9,555 fish were collected, distributed across 25 families and seven orders (Tab. S4). The Characiformes accounted for over 65% of all individuals sampled, followed by Cichliformes, Gymnotiformes, and Siluriformes, respectively. Of this total, 3,187 specimens were sampled from streams intersected by the buried pipeline, while 6,368 were found in control area streams. Fish from the Characidae family were highly abundant in both environments, followed by those from the Lebiasinidae and Cichlidae.

Fish species richness ranged from one to 26 species among the streams, with 26 species recorded in control streams and 25 in pipeline streams. Similar families of fish were observed in both pipeline-influenced and control streams. The t-test did not detect any difference in species richness between the categories (t = 0.150, df = 51, p = 0.882).

The analysis of covariance (ANCOVA) highlighted the influence of selected variables on fish species richness between the categories (Figs. 2A–C). Species richness showed a negative relationship with fine roots (R² = -0.460, p = 0.007; Fig. 2A) and thalweg depth (Depth T; R² = -0.460, p = 0.006; Fig. 2B), and a positive relationship with dissolved oxygen (R² = 0.400, p = 0.022; Fig. 2C). No effects of the environmental variables on species richness were detected specifically within the pipeline category.

FIGURE 2| Relationship between environmental variables and species richness of streams between the Control and Pipeline categories.

The distance-based redundancy analysis (db-RDA) revealed a significant relationship between local environmental conditions and local-scale land use features on the stream fish species composition (F = 2.55; p = 0.001). Approximately 18% of the total inertia in dissimilarities was explained by the first two db-RDA axes. Composition showed a negative correlation with filamentous algae shelter (“shelter algae”) and temperature (“TEMP”) and a positive correlation with seasonal bed height (“seasonal height”) and fine roots (“fine roots”) along Axis 1. Meanwhile, Axis 2 exhibited a negative correlation with local-scale land use (PC1), the first axis of spatial variables (AEM1), and channel depth (“depth_T”) (Fig. 3). Twenty-nine fish species were significantly associated with the db-RDA axes (Tab. S5). Streams with higher amounts of filamentous algae tended to have greater abundances of Iguanodectes rachovii and Bryconops caudomaculatus, while streams with higher seasonal bed height values and fine root density supported greater abundances of Ituglanis amazonicus, Moenkhausia oligolepis, and Hyphessobrycon sp. The local-scale land use was positively associated with agriculture and negatively with forest cover, while spatial variables were more closely associated with species such as Moenkhausia sp. and Pyrrhulina capim (Fig. 3).

FIGURE 3| Distance-based redundancy analysis (db-RDA) relating environmental and landscape variables to the species composition of fish collected in the sampled streams. Variable codes: Shelter algae; Seasonal bed height; Landscape PC1; Depth T; Temperature TEMP; Fine roots. Species code: Carnegiella strigata (Cstrig); Bunocephalus coracoideus (Bcora); Hyphessobrycon sp. (Hyphsp); Ituglanis amazonicus (Iamaz); Hemigrammus bellottii (Hbell); Moenkhausia oligolepis (Moligol); Hemmigrammus sp. (Hemmsp); Nannostomus eques (Neques); Characidium sp. (Chasp); Iguanodectes rachovii (Iracho); Iguanodectes sp. (Iguasp); Moenkhausia sp. (Moenksp); Erythrinus erythrinus (Eery); Pyrrhulina capim (Pcapi); Anablepsoides urophthalmus (Aurop); Copella arnoldi (Carnol); Hyphessobrycon heterorhabdus (hhete); Bryconops caudomaculatus (Bcau).

We identified a significant difference in fish species composition between categories (PERMANOVA: F₁,₅₁ = 2.2916, p = 0.016); however, dispersion between categories was similar (PERMDISP: F₁,₅₁ = 1.2985, p = 0.2598). Variance partitioning analysis revealed that only environmental factors significantly explained the variation in fish species richness in streams (adjusted R² = 0.30; p = 0.001; Fig. 4A). Similarly, for species composition, only local environmental variables accounted for the observed variation (adjusted R² = 0.12; p = 0.001; Fig. 4B). Both local-scale land use nor spatial variables contributed to the variation in fish species richness or composition in the studied streams.

FIGURE 4| Venn diagram of variance partitioning, showing environment, landscape and space contributions to stream fish species richness (A) and composition (B). Values ​​indicate the percentage of variation explained by each fraction. Permutation tests were calculated on the variation explained by each testable fraction (p < 0.05).

When we evaluated the internal variation of the stream, i.e., comparing the stretches within a stream, no significant differences were observed in the composition of fish species between the upstream and downstream stretches of the streams under the influence of pipeline (PERMANOVA: R² = 0.01, F = 0.24, p = 0.99). There were also no significant differences in the composition of fish species between the upstream and downstream stretches of the control streams (PERMANOVA: R² = 0.01, F = 0.43, p = 0.95).

Nevertheless, when comparing stream reaches between the two categories (control vs. pipeline), both upstream and downstream, we observed significant differences in fish community composition. Specifically, comparisons of upstream reaches between control and pipeline streams revealed significant differences in species composition (PERMANOVA: R² = 0.04, F = 2.24, p = 0.02; Fig. 5A), as did comparisons of downstream reaches (PERMANOVA: R² = 0.05, F = 2.47, p = 0.01; Fig. 5B).

FIGURE 5| Analysis of PCoA with the difference in fish species composition between the upstream and downstream stretches of the streams.

Discussion​

The results revealed that although species richness was similar between the control streams and those intersected by the buried pipeline, the composition of the ichthyofauna differed significantly between the categories. We found that local environmental variables, such as fine roots, channel depth and dissolved oxygen, significantly influenced species richness, highlighting the importance of local environmental conditions for maintaining diversity. In addition, factors such as the presence of filamentous algae and the seasonal height of the bed contributed to the differences observed in species composition, with certain taxa associated with specific conditions. In contrast, local-scale land use and spatial variables did not explain the variation in species richness or composition, reinforcing the predominance of local environmental factors in structuring these communities. Analysis of the upstream and downstream stretches of the streams also revealed significant differences in the composition of fish species, suggesting that indirect alterations associated with surrounding infrastructure may contribute to local shifts in ichthyofaunal composition, even without the presence of visible flow disruption.

The lack of significant impact on species richness contradicts initial expectations, since previous studies indicate that large infrastructure projects, such as mining pipelines, can cause significant damage to aquatic ecosystems, negatively altering fish communities (Freitas et al., 2018; Ellwanger et al., 2024). However, because the pipeline in this study is buried and sometimes does not obstruct stream flow and affect punctually the physical streambeds, it is possible that the observed differences are more closely linked to the presence of associated infrastructure, such as access roads or to cumulative landscape changes. One plausible explanation is that the resilience of communities and the adaptive capacity of species contribute to maintaining biodiversity, even under anthropogenic stressors (Moura et al., 2012). These findings highlight the importance of implementing integrated environmental management strategies and carefully assessing the potential impacts of large projects from the initial planning stages. It should be noted, however, that the absence of detectable effects in this study does not exclude the possibility of indirect or long-term impacts, and continuous monitoring is essential to ensure the sustainability of industrial activities (Lepori, Hjerdt, 2006; Ogidi, Akpan, 2022).

Changes in habitat structure, such as substrate alteration and/or temperature changes directly affect aspects of community structure such as richness, abundance, and community composition. Numerous studies have confirmed these observations, especially regarding fish communities, given their ability to exploit different habitats (Gorman, Karr, 1978; Romanuk et al., 2006; Leitão et al., 2017; Volta, Jeppesen, 2021). However, our results showed that when compared between the categories used in this study (control and pipeline), fish species richness did not respond to these alterations, contrary to our hypothesis. It is worth noting that the effects of anthropogenic disturbances influence the distribution of certain species, meaning that the impacts are not uniform across all taxa within the community (Brooker et al., 2020; Firmiano et al., 2021; Larentis et al., 2022). Our data suggest that environmental variables significantly influence fish species richness in the studied streams, with different response patterns observed between streams with and without direct pipeline influence. The negative relationship between fish species richness and variables such as fine roots and thalweg depth in streams unaffected by the pipeline aligns with previous studies highlighting the importance of structure and environmental heterogeneity for fish diversity (Liao et al., 2023; Mao et al., 2024).

Additionally, the positive relationship observed between species richness and dissolved oxygen in these streams underscores the importance of water quality in supporting aquatic life (Bulbul Ali, Mishra, 2022). However, the lack of a statistical relationship between environmental variables and species richness in pipeline-affected streams may indicate that other factors, such as direct or indirect pipeline effects, may outweigh the influence of environmental variables commonly used in studies, such as pH, temperature, and oxygen. These results highlight the complexity of interactions between environmental and anthropogenic factors in structuring fish communities in streams, reaffirming the importance of considering not only physicochemical variables but also anthropogenic factors in monitoring aquatic ecosystems (Mitra, 2019; Pinna et al., 2023).

The influence of the local environment and local-scale land use on fish species composition in streams is crucial for understanding how different ecological factors shape aquatic communities. At the local level, environmental characteristics such as the presence of filamentous algae shelters, water temperature, and substrate structure play key roles in determining species abundance and distribution. For instance, filamentous algae provide refuge and food for certain species, such as Iguanodectes ranchovii and Bryconops caudomaculatus. These species are known to prefer environments with dense vegetation cover and more complex habitat structures, which offer protection from predators and support prey capture (Esteves et al., 2021). Water temperature, in turn, can influence species metabolism and feeding behavior, creating more favorable conditions for some species (Santos et al., 2022).

Additionally, substrate structure also plays a significant role. A more heterogeneous substrate, comprising a combination of different cover types and structures, provides a range of microhabitats for species. Genera such as Ituglanis and Moenkhausia seem to benefit from this diversity of microhabitats, consistent with studies emphasizing the importance of environmental heterogeneity in maintaining fish diversity in riverine ecosystems (Baltz et al., 1991; Peres-Neto, 2004). Substrate variation offers distinct habitat where different species can coexist and thrive. Areas with greater forest cover can act as resource sources, providing organic matter and detritus that fuel aquatic food webs and serve as barriers against anthropogenic degradation, such as erosion and pollution (Siqueira et al., 2012). In contrast, areas with exposed soil or intensive land use, such as mining, may reduce water quality, alter habitat availability, and negatively affect aquatic communities (Souza et al., 2020).

The relationship between local factors and the local-scale land use reinforces the notion that the “health” and diversity of aquatic ecosystems depend on both immediate factors, such as habitat structure, and broader local-scale land use influences. This complex interaction between local and regional levels underscores the need for integrated management that considers not only the preservation of aquatic habitats but also land use in adjacent areas to mitigate adverse impacts and promote biodiversity conservation (Pereira et al., 2022). Among the sampled streams, only local environmental variables were significant in explaining the variation in fish species richness and composition, while local-scale land use and spatial variables showed no significant association. This finding highlights the importance of local physical habitat aspects in determining the structure of fish communities in streams, suggesting that characteristics such as channel morphology, substrate, and shelter availability may play a predominant role in ecosystem processes (Leprieur et al., 2011).

The lack of influence from local-scale land use variables, such as different land uses, aligns with studies emphasizing the importance of spatial scale in fish community responses to environmental gradients. In aquatic environments, local habitat characteristics tend to have a more direct and immediate influence on species distribution and abundance, whereas the effects of landscape patterns may be subtler and manifest over broader spatial scales (Leitão et al., 2017). In this study, the landscape assessment occurred at a local scale, in the vicinity of the sampled points. While the negative impact of different land uses on species diversity in terrestrial environments is widely discussed (Bicknell, Peres, 2010; Bicknell et al., 2015), studies on aquatic biota have revealed contrasting results. Some research points to detrimental effects on the habitat specialization of aquatic communities, whereas others have not observed significant changes (Mykrä, Heino, 2017).

The similarity of the ichthyofauna observed between the categories, control and pipeline, may be related to the greater forest cover surrounding the streams in the pipeline area. Although the region still retains extensive areas of native vegetation, the 30-meter buffer established by the project acts as a mitigation measure, attenuating the negative effects of removing vegetation outside its immediate boundaries. Although it does not constitute an impenetrable barrier to deforestation, this stretch can reduce the intensity of anthropogenic disturbances on adjacent streams (Sweeney, Newbold, 2014; Brejão et al., 2020). The maintenance of riparian vegetation contributes to bank stabilization, channel shading and the maintenance of hydrological connectivity, which are important factors for the ecological integrity of aquatic ecosystems (Vieira et al., 2015; Brejão et al., 2020).

The preservation of riparian vegetation contributes to the partial maintenance of hydrological connectivity, especially in the longitudinal dimension, by favoring the continuous flow of water, organic matter, and organisms along the river course (Vannote et al., 1980; Pringle, 2001). This connectivity can facilitate the movement of species between upstream and downstream reaches, potentially reducing local exclusion and helping to maintain relatively stable species richness, even in altered reaches. In the case of streams affected by the pipeline, the presence of well-preserved riparian vegetation may have mitigated more pronounced structural differences between upstream and downstream reaches, providing critical resources — such as shelter and food — that support the persistence of fish assemblages under anthropogenic pressure. According to Karr (1981), biological communities can recover from disturbances when external intervention is limited. This context may favor the expansion of generalist or opportunistic species (Casatti et al., 2010), capable of exploiting altered habitats. Therefore, although physical or hydrological disturbances may occur, the ecological function of riparian vegetation likely plays a key role in buffering impacts and maintaining taxonomic diversity at the local scale.

The current ichthyofaunal structure observed in streams affected by mining may be partially attributed to the long history of human exploitation in the region, particularly due to over 30 years of bauxite extraction. This activity leads to cumulative environmental degradation, including changes in water quality, sediment composition, and habitat structure, which, over time, tend to select for more disturbance-tolerant species. Although local-scale land variables did not show significant effects in our models, the db-RDA results revealed a weak but detectable relationship between species composition and environmental variation. Similar findings have been reported in other studies, where long-term anthropogenic pressure reduced fish diversity and favored generalist or resilient species capable of withstanding increased turbidity, reduced structural complexity, and fluctuations in water quality (Junk et al., 2007; Leal et al., 2016). This pattern of biotic homogenization has also been linked to persistent land use and mining activities in various neotropical basins (Castro, Polaz, 2020).

The absence of significant differences in species composition between the upstream and downstream stretches within the same stream, both in the control streams and in those under the influence of the pipeline, suggests that the ichthyofauna responds relatively homogeneously to local environmental conditions on a small spatial scale. This pattern may be related to habitat continuity and limited longitudinal heterogeneity in small streams (Ligeiro et al., 2013), which allows species to disperse and reduces community differentiation along the course of the stream (Siqueira et al., 2012). On the other hand, when comparing the stretches between land use categories (control vs. pipeline), both upstream and downstream, we observed significant differences in community composition. This indicates that anthropogenic land use, associated with the implementation of the pipeline, promotes environmental changes sufficient to modify the assembly of fish species, even when considering equivalent stretches of the hydrographic network. These results corroborate studies showing that changes in land use and cover affect the composition of the ichthyofauna, mainly through habitat degradation, increased sedimentation, changes in connectivity and water quality (Leitão et al., 2017; Teresa, Casatti, 2017).

The replacement of sensitive species by more tolerant taxa in impacted areas may be indicative of biotic homogenization processes (Olden et al., 2004), which reinforces the need for continuous monitoring in areas under the influence of mining activities. While the negative impact of land use on diversity is widely documented for terrestrial environments (Bicknell, Peres, 2010; Bicknell et al., 2015), the effects on aquatic biota are less consistent. Some studies suggest damage to the specialization of communities, while others do not detect significant changes (Mykrä, Heino, 2017). Species with more restricted niches tend to be more susceptible to environmental disturbances (Le Feuvre et al., 2021).

The results reveal that, although species richness did not differ significantly between control streams and those influenced by the pipeline, striking differences in species composition point to substantial changes in taxonomic diversity and community structure. These findings reinforce the importance of examining not only species counts but also species identity and turnover in response to environmental change. Our analyses indicate that local environmental variables are the primary factors shaping fish assemblages in these Amazonian streams, while local-scale land use and spatial factors played a comparatively minor role. This suggests that small-scale habitat conditions may override large-scale influences, highlighting the critical need to preserve local habitat quality. Given the ecological sensitivity and high biodiversity of Amazonian freshwater systems, these taxonomic changes can have cascading effects on ecosystem functioning. Therefore, long-term biomonitoring efforts are crucial to track potential biodiversity losses and guide evidence-based conservation and management. Furthermore, integrating ecological knowledge, particularly on taxonomic composition and functional characteristics, into infrastructure planning is essential to reduce impacts and promote sustainable land use practices in tropical forest regions.

Taken together, our findings suggest that, although the buried mining pipeline may represent a lower-impact alternative to conventional surface-level transportation routes, due to its subsurface placement and lack of surface impoundment, indirect effects associated with ancillary infrastructure, particularly service roads, still warrant targeted mitigation (Reddy et al., 2023). These roads can modify riparian vegetation, increase sediment runoff, and compromise local environmental integrity, ultimately influencing fish assemblages even in the absence of direct hydrological alteration. The observed differences in species composition between impacted and control streams, especially across upstream and downstream reaches, underscore the importance of considering both direct and diffuse impacts when assessing the ecological footprint of infrastructure projects in sensitive ecosystems.

Acknowledgments​

We thank the anonymous reviewers and academic editors for their valuable contributions. We also express our sincere gratitude to the field team at the Laboratory of Ecology and Conservation (LABECO/UFPA) for their essential support during the fieldwork and data collection stages of this study. The dedication and professionalism of LABECO members were crucial to the success of the research activities. We would like to thank Norsk Hydro for funding the project which enabled us to collect and obtain the data for this research.

References​

Allan JD. Landscapes and riverscapes: the influence of land use on stream ecosystems. Ann Rev Ecol Evol Syst. 2004; 35(1):257–84. https://doi.org/10.1146/annurev.ecolsys.35.120202.110122

Baltz DM, Vondracek B, Brown LR, Moyle PB. Seasonal changes in microhabitat selection by rainbow trout in a small stream. Trans Am Fish Soc. 1991; 120(2):166–76. https://doi.org/10.1577/1548-8659(1991)120<0166:SCIMSB>2.3.CO;2

Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F. Impacts of climate change on the future of biodiversity. Ecol Lett. 2012; 15(4):365–77. https://doi.org/10.1111/j.1461-0248.2011.01736.x

Benone NL, Leal CG, Santos LL, Mendes TP, Heino J, Montag LFA. Unravelling patterns of taxonomic and functional diversity of Amazon stream fish. Aquat Sci. 2020; 82:1–11. https://doi.org/10.1007/s10750-014-1925-2

Benone NL, Ligeiro R, Juen L, Montag LFA. Role of environmental and spatial processes structuring fish assemblages in streams of the eastern Amazon. Mar Freshw Res. 2018; 69(2):243–52. https://doi.org/10.1071/MF17103

Bicknell J, Peres CA. Vertebrate population responses to reduced-impact logging in a neotropical forest. For Ecol Manag. 2010; 259(12):2267–75. https://doi.org/10.1016/j.foreco.2010.02.027

Bicknell JE, Struebig MJ, Davies ZG. Reconciling timber extraction with biodiversity conservation in tropical forests using reduced-impact logging. J Appl Ecol. 2015; 52(2):379–88. https://doi.org/10.1111/1365-2664.12391

Blanchet FG, Legendre P, Maranger R, Monti D, Pepin P. Modelling the effect of directional spatial ecological processes at different scales. Oecologia. 2011; 166:357–68. https://doi.org/10.1007/s00442-010-1867-y

Borcard D, Legendre P. All-scale spatial analysis of ecological data by means of main coordinates of neighbor matrices. Ecol Model. 2002; 153:51–68.

Brejão GL, Hoeinghaus DJ, Roa-Fuentes CA, Pérez-Mayorga MA, Ferraz SF, Casatti L. Taxonomic and functional turnover of Amazonian stream fish assemblages is determined by deforestation history and environmental variables at multiple scales. Neotrop Ichthyol. 2021; 19(3):e210042. https://doi.org/10.1590/1982-0224-2021-0042

Brejão GL, Teresa FB, Gerhard P. When roads cross streams: fish assemblage responses to fluvial fragmentation in lowland Amazonian streams. Neotrop Ichthyol. 2020; 18(3):e200031. https://doi.org/10.1590/1982-0224-2020-0031

Britski HA, Sillimon KZS, Lopes BS. Peixes do Pantanal: manual de identificação. Brasília: Embrapa; 2007.

Brooker MA, Lestang S, Fairclough DVi, McLean D, Slawinski D, Pember MB et al. Environmental and anthropogenic factors affect fish abundance: relationships revealed by automated cameras deployed by fishers. Front Mar Sci. 2020; 7:279. https://doi.org/10.3389/fmars.2020.00279

Brumbaugh WG, Schmitt CJ, May TW. Concentrations of cadmium, lead, and zinc in fish from mining-influenced waters of northeastern Oklahoma: sampling of blood, carcass, and liver for aquatic biomonitoring. Arch Environ Contam Toxicol. 2005; 49:76–88. https://doi.org/10.1007/s00244-004-0172-3

Bulbul Ali A, Mishra A. Effects of dissolved oxygen concentration on freshwater fish: a review. Int J Fish Aquat Stud. 2022; 10(4):113–27. https://doi.org/10.22271/fish.2022.v10.i4b.2693

Burcher CL, McTammany ME, Benfield EF, Helfman GS. Fish assemblage responses to forest cover. Environ Manag. 2008; 41:336–46. https://doi.org/10.1007/s00267-007-9049-3

Callisto M, Alves CBM, Lopes JM, Castro MA. Condições ecológicas em bacias hidrográficas de empreendimentos hidrelétricos. CEMIG, Belo Horizonte. 2014.

Castro RMC, Polaz CNM. Small-sized fish: the largest and most threatened portion of the megadiverse neotropical freshwater fish fauna. Biota Neotrop. 2020; 20(1):e20180683. https://doi.org/10.1590/1676-0611-BN-2018-0683

Carvalho FG, Pinto NS, Oliveira Júnior JMB, Juen L. Effects of marginal vegetation removal on Odonata communities. Acta Limnol Bras. 2013; 25(1):10–18. https://doi.org/10.1590/S2179-975X2013005000013

Casatti L, Langeani F, Silva AM, Castro RMC. Stream fish, water and habitat quality in a pasture dominated basin, southeastern Brazil. Braz J Biol. 2006; 66(2):681–96. https://doi.org/10.1590/S1519-69842006000400012

Casatti L, Teresa FB, Sabino J, Langeani F. Estrutura da comunidade de peixes ao longo de um gradiente de conservação em riachos do Planalto da Bodoquena, centro-oeste do Brasil. Acta Limnol Bras. 2010; 22(1):50–59. https://doi.org/10.4322/actalb.02201007

Cunha EJ, Montag LFA, Juen L. Oil palm crops effects on environmental integrity of Amazonian streams and Heteropteran (Hemiptera) species diversity. Ecol Indic. 2015; 52:422–29. https://doi.org/10.1016/j.ecolind.2014.12.024

Dray S, Blanchet G, Borcard D, Guenard G, Jombart T, Larocque G et al. Package ‘adespatial’. R package. 2012; 3–08.

Ellwanger JH, Fearnside PM, Ziliotto M, Chies JAB. Pollution-related biodiversity loss in Brazil: more actions required. WAPLAC. 2024; 235(8):1–05. https://doi.org/10.1007/s11270-024-07332-w

Esteves KE, Aranha JMR, Albrecht MP. Ecologia trófica de peixes de riacho: uma releitura 20 anos depois. Oecol Austr. 2021; 25(2):266–82. https://doi.org/10.4257/oeco.2021.2502.04

Ficke AD, Myrick CA, Hansen LJ. Potential impacts of global climate change on freshwater fisheries. Rev Fish Biol Fish. 2007; 17:581–613. https://doi.org/10.1007/s11160-007-9059-5

Firmiano KR, Castro DM, Linares MS, Callisto M. Functional responses of aquatic invertebrates to anthropogenic stressors in riparian zones of Neotropical savanna streams. Sci Total Environ. 2021; 753:141865. https://doi.org/10.1016/j.scitotenv.2020.141865

Freitas CEC, Laurenson L, Yamamoto KC, Forsberg BR, Petrere Jr M, Arantes C et al. Fish species richness is associated with the availability of landscape components across seasons in the Amazonian floodplain. PeerJ. 2018; 6:e5080. https://doi.org/10.7717/peerj.5080

Freitas PV, Montag LFA, Ilha P, Torres NR, Maia C, Deegan L et al. Local effects of deforestation on stream fish assemblages in the amazon-savannah transitional area. Neotrop Ichthyol. 2021; 19:e210098. https://doi.org/10.1590/1982-0224-2021-0098

Gomes HI, Mayes WM, Rogerson M, Stewart DI, Burke IT. Alkaline residues and the environment: a review of impacts, management practices and opportunities. J Clean Prod. 2016; 112:3571–82. https://doi.org/10.1016/j.jclepro.2015.09.111

Gorman OT, Karr JR. Habitat structure and stream fish communities. Ecology. 1978; 59(3):507–15.

Harding JS, Benfield EF, Bolstad PV, Helfman GS, Jones IEBD. Stream biodiversity: the ghost of land use past. PNAS. 1998; 95(25):14843–47. https://doi.org/10.1073/pnas.95.25.14843

Hartig F. DHARMa: residual diagnostics for hierarchical (Multi-Level / Mixed) regression models. Rpackage version 0.4.6. 2022. Available from: https://CRAN.R-project.org/package=DHARMa

Iwata T, Nakano S, Inoue M. Impacts of past riparian deforestation on stream communities in a tropical rain forest in Borneo. Ecol Appl. 2003; 13(2):461–73. https://doi.org/10.1890/1051-0761(2003)013[0461:IOPRDO]2.0.CO;2

Joy MK, Foote KJ, McNie P, Piria M. Decline in New Zealand’s freshwater fish fauna: effect of land use. Mar Freshw Res. 2019; 70(1):114–24. https://doi.org/10.1071/MF18028

Juen L, Cunha EJ, Carvalho FG, Ferreira MC, Begot TO, Andrade AL et al. Effects of oil palm plantations on the habitat structure and biota of streams in Eastern Amazon. River Res Appl. 2016; 32(10):2081–94. https://doi.org/10.1002/rra.3050

Junk WJ, Soares MGM, Bayley PB. Freshwater fishes of the Amazon River basin: their biodiversity, fisheries, and habitats. Aqua Ecos Heal Manag. 2007; 10(2):153–73. https://doi.org/10.1080/14634980701351023

Karr JR. Assessment of biotic integrity using fish communities. Fisheries. 1981; 6(6):21–27.

Kaufmann PR, Levine P, Peck DV, Robison EG, Seeliger C. Quantifying physical habitat in wadeable streams. USEPA, National Health and Environmental Effects Research Laboratory, Western Ecology Division; 1999.

Kerr SR, Werner EE. Niche theory in fisheries ecology. Trans Am Fish Soc. 1980; 109(2):254–60.

Kullander SO. Cichlid fishes of the Amazon River drainage of Peru. Department of Vertebrate Zoology, Research Division, Swedish Museum of Natural History. 1986.

Larentis C, Kotz KBC, Neves MP, Delariva RL. Effects of human disturbance on habitat and fish diversity in Neotropical streams. PLoS ONE. 2022; 17(9):e0274191. https://doi.org/10.1371/journal.pone.0274191

Leal CG, Pompeu PS, Gardner TA, Leitão RP, Hughes RM, Kaufmann PR et al. Multi-scale assessment of human-induced changes to Amazonian instream habitats. Landsc Ecol. 2016; 31:1725–45. https://doi.org/10.1007/s10980-016-0358-x

Leitão RP, Zuanon J, Mouillot D, Leal CG, Hughes RM, Kaufmann PR et al. Disentangling the pathways of land use impacts on the functional structure of fish assemblages in Amazon streams. Ecography. 2017; 41:219–32. https://dx.doi.org/10.1111/ecog.02845

Legendre P, Anderson MJ. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol Monogr. 1999; 69(1):1–24.

Lepori F, Hjerdt N. Disturbance and aquatic biodiversity: reconciling contrasting views. BioScience. 2006; 56(10):809–18. https://doi.org/10.1641/00063568(2006)56[809:DAABRC]2.0.CO;2

Leprieur F, Tedesco PA, Hugueny B, Beauchard O, Dürr HH, Brosse S et al. Partitioning global patterns of freshwater fish beta diversity reveals contrasting signatures of past climate changes. Ecol Lett. 2011; 14(4):325–34. https://doi.org/10.1111/j.1461-0248.2011.01589.x

Lévesque LM, Dubé MG. Review of the effects of in-stream pipeline crossing construction on aquatic ecosystems and examination of Canadian methodologies for impact assessment. Environmental monitoring and assessment. 2007; 132:395–409. https://doi.org/10.1007/s10661-006-9542-9

Liao C, Ye S, Zhai D, Yu J, Correa SB, Wen F et al. Tributaries create habitat heterogeneity and enhance fish assemblage variation in one of the largest reservoirs in the world. Hydrobiologia. 2023; 850(19):4311–26. https://doi.org/10.1007/s10750-023-05306-3

Ligeiro R, Hughes RM, Kaufmann PR, Macedo DR, Firmiano KR, Ferreira WR et al. Defining quantitative stream disturbance gradients and the additive role of habitat variation to explain macroinvertebrate taxa richness. Ecol Ind. 2013; 25:45–57. https://doi.org/10.1016/j.ecolind.2012.09.004

Le Feuvre MC, Dempster T, Shelley JJ, Davis AM, Swearer SE. Range restriction leads to narrower ecological niches and greater extinction risk in Australian freshwater fish. Biodivers Conserv. 2021; 30(11):2955–76. https://doi.org/10.1007/s10531-021-02229-0

Macedo M, Castello L. State of the Amazon: freshwater connectivity and ecosystem health. Brasília: WWF Living Amazon Initiative; 2015.

Mao Z, Gu X, Zeng Q, Chen H, Jeppesen E. Effects of habitat regime type on fish diversity in a large eutrophic lake. Hydrobiologia. 2024; 851(7):1807–23. https://doi.org/10.1007/s10750-023-05416-y

MapBiomas. Projeto MapBiomas – Coleção Beta de Mapas Anuais de Cobertura e Uso da Terra do Brasil com 10 metros de resolução espacial. Available from: https://brasil.mapbiomas.org/mapbiomas-cobertura-10m/

Martínez-Meyer E, Díaz-Porras D, Peterson AT, Yáñez-Arenas C. Ecological niche structure and rangewide abundance patterns of species. Biol Lett. 2013; 9(1):20120637. https://doi.org/10.1098/rsbl.2012.0637

McArdle BH, Anderson MJ. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology. 2001; 82(1):290–97. https://doi.org/10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2

Mechi A, Sanches DL. Impactos ambientais da mineração no estado de São Paulo. Estudos Avançados. 2010; 68:209–20.

Mendonça FP, Magnusson WE, Zuanon J. Relationships between habitat characteristics and fish assemblages in small streams of Central Amazonia. Copeia. 2005(4):751–64. https://doi.org/10.1643/0045-8511(2005)005[0751:RBHCAF]2.0.CO;2

Mitra A. Monitoring and management of pollution level. In: Estuarine pollution in the lower Gangetic Delta. Cham: Springer; 2018. p.321–63. https://doi.org/10.1007/978-3-319-93305-4_8

Montag LFA, Winemiller KO, Keppeler FW, Leão H, Benone NL, Torres NR et al. Land cover, riparian zones and instream habitat influence stream fish assemblages in the eastern Amazon. Ecol Freshw Fish. 2019; 28(2):317–29. https://doi.org/10.1111/eff.12455

Monteiro-Júnior CS, Juen L, Hamada N. Effects of urbanization on stream habitats and associated adult dragonfly and damselfly communities in central Brazilian Amazonia. Landsc Urban Plan. 2014; 127:28–40. https://doi.org/10.1016/j.landurbplan.2014.03.006

Moura PM, Vieira JP, Garcia AM. Fish abundance and species richness across an estuarine–freshwater ecosystem in the Neotropics. Hydrobiologia. 2012; 696:107–22. https://doi.org/10.1007/s10750-012-1187-9

Mykrä H, Heino J. Decreased habitat specialization in macroinvertebrate assemblages in anthropogenically disturbed streams. Ecol Complex. 2017; 31:181–88. https://doi.org/10.1016/j.ecocom.2017.07.002

Negrão LBA, Costa MLD, Pöllmann H. The Belterra clay on the bauxite deposits of Rondon do Pará, Eastern Amazon. Braz J Geol. 2018; 48:473–84. https://doi.org/10.1590/2317-4889201820180128

Negrão LBA, Pöllmann H, Cortinhas ATK. Mineralogical appraisal of bauxite overburdens from Brazil. Minerals. 2021;11(7):677. https://doi.org/10.3390/min11070677

Ogidi OI, Akpan UM. Aquatic biodiversity loss: impacts of pollution and anthropogenic activities and strategies for conservation. In: Chibueze Izah S, editor. Biodiversity in Africa: potentials, threats and conservation. Sustainable development and biodiversity. 2022; 29. https://doi.org/10.1007/978-981-19-3326-4_16

Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB et al. Vegan: community ecology package. R package version 2.3-0. R Foundation for Statistical Computing, Vienna. 2015.

Olden JD, Poff NL. Processos ecológicos que impulsionam a homogeneização biótica: testando um modelo mecanicista usando faunas de peixes. Ecologia. 2004; 85(7):1867–75. https://doi.org/10.1890/03-3131

Ortega JCG; Bacani I, Dorado-Rodrigues TF, Strüssmann CF, Izaias M, Morales J et al. Effects of urbanization and environmental heterogeneity on fish assemblages in small streams. Neotrop Ichthyol. 2021; 19(3):e210050. http://dx.doi.org/10.1590/1982-0224-2021-0050

Palheta GS, Benone NL, Santos RL, Montag LFA. Unraveling the role of environmental factors and dispersal capacity in a metacommunity of Amazonian stream fishes. Aquatic Ecol. 2021; 55(1):227–36. https://doi.org/10.1007/s10452-020-09824-3

Peck DV, Averill DK, Herlihy AT, Hughes RM, Kaufmann PR, Klemm DJ et al. Cappaert. Environmental monitoring and assessment program-surface water western pilot study: field operations manual for wadeble streams, U.S. Environmental Protection Agency, Office of Research and Development, Washington, D.C. 2005.

Pereira P, Inacio M, Kalinauskas M, Bogdzevič K, Bogunovic I, Zhao W. Land-use changes and ecosystem services. In: Pereira P, Gomes E, Rocha J, editors. Mapping and forecasting land use. Elsevier; 2022. p.1–27. https://doi.org/10.1016/B978-0-323-90947-1.00007-7

Peres-Neto PR. Patterns in the co-occurrence of fish species in streams: the role of site suitability, morphology and phylogeny versus species interactions. Oecologia. 2004; 140(2):352–60. https://doi.org/10.1007/s00442-004-1578-3

Peres-Neto PR, Legendre P. Estimating and controlling for spatial structure in the study of ecological communities. Glob Ecol Biogeogr. 2010; 19:174–84. https://doi.org/10.1111/j.1466-8238.2009.00506.x

Pinna M, Zangaro F, Saccomanno B, Scalone C, Bozzeda F, Fanini L et al. An overview of ecological indicators of fish to evaluate the anthropogenic pressures in aquatic ecosystems: from traditional to innovative DNA-based approaches. Water. 2023; 15(5):949. https://doi.org/10.3390/w15050949

Pinto BCT, Araujo FG, Rodrigues VD, Hughes RM. Local and ecoregion effects on fish assemblage structure in tributaries of the rio Paraíba do Sul, Brazil. Freshw Biol. 2009; 54(12):2600–15. https://doi.org/10.1111/j.1365-2427.2009.02269.x

Poff NL. Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. J N Am Benthol Soc. 1997; 16(2):391–409. https://doi.org/10.2307/1468026

Pringle CM. Hydrologic connectivity and the management of biological reserves: a global perspective. Ecol Appl. 2001; 11(4):981–98. https://doi.org/10.1890/1051-0761(2001)011[0981:HCATMO]2.0.CO;2

Prudente BS, Pompeu PS, Juen L, Montag LFA. Effects of reduced-impact logging on physical habitat and fish assemblages in streams of Eastern Amazonia. Freshw Biol. 2017; 62(2):303–16. https://doi.org/10.1111/fwb.12868

R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna: Austria. 2023. Available from: https://www.R-project.org/

Reddy NVK, Pothal JK, Barik R, Senapati PK. Pipeline slurry transportation system: an overview. J Pipeline Syst Eng Pract. 2023; 14:03123001. https://doi.org/10.1061/JPSEA2.PSENG-1391

Richter BD, Braun DP, Mendelson MA, Master LL. Threats to imperiled freshwater fauna: amenazas a la fauna dulceacuicola en riesgo. Conserv Biol. 1997; 11(5):1081–93. https://doi.org/10.1046/j.1523-1739.1997.96236.x

Roa-Fuentes CA, Casatti L. Influence of environmental features at multiple scales and spatial structure on stream fish communities in a tropical agricultural region. J Freshw Ecol. 2017; 32(1):281–95. https://doi.org/10.1080/02705060.2017.1287129

Romanuk TN, Jackson LJ, Post JR, McCauley E, Martinez ND. The structure of food webs along river networks. Ecography. 2006; 29(1):3–10. https://doi.org/10.1111/j.2005.0906-7590.04181.x

Santos LL, Benone NL, Soares BE, Barthem RB, Montag LFA. Trait–environment relationships in Amazon stream fish assemblages. Ecol Freshw Fish. 2019; 28(3):424–33. https://doi.org/10.1111/eff.12465

Scheffer M, Carpenter SR. Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol Evol. 2003; 18(12):648–56. https://doi.org/10.1016/j.tree.2003.09.002

Seabra LB, Benone NL, Montag LFA. Assessing the effects of multiple land uses on the functional beta diversity of stream fishes in the Amazon region. Hydrobiologia. 2022; 849(20):4515–27. https://doi.org/10.1007/s10750-020-04512-7

Siqueira T, Bini LM, Roque FO, Marques CSR, Trivinho-Strixino S, Cottenie K. Common and rare species respond to similar niche processes in macroinvertebrate metacommunities. Ecography. 2012; 35(2):183–92. https://doi.org/10.1111/j.1600-0587.2011.06875.x

Souza Jr CM, Shimbo JZ, Rosa MR, Parente LL, Alencar AA, Rudorff BF et al. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and earth engine. Remote Sens. 2020; 12(17):2735. https://doi.org/10.3390/rs12172735

Strahler AN. Quantitative analysis of watershed geomorphology. EOS Trans Am Geophys Union. 1957; 38(6):913–20.

Sweeney BW, Newbold JD. Streamside forest buffer width needed to protect stream water quality, habitat, and organisms: a literature review. JAWRA. 2014; 50(3):560–84. https://doi.org/10.1111/jawr.12203

Teresa FB, Casatti L. Trait-based metrics as bioindicators: responses of stream fish assemblages to a gradient of environmental degradation. Ecol Ind. 2017; 75:249–58. https://doi.org/10.1016/j.ecolind.2016.12.041

Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE. The river continuum concept. CJFAS. 1980; 37(1):130–37. https://doi.org/10.1139/f80-017

Vieira TB, Dias-Silva K, Pacífico EDS. Effects of riparian vegetation integrity on fish and Heteroptera communities. Appl Ecol Environ Res. 2015; 13(1):53–65. https://doi.org/10.15666/aeer/1301_053065

Volta P, Jeppesen E. Impacts of human activities and climate change on freshwater fish. Water. 2021; 13(21):3068. https://doi.org/10.3390/w13213068

Zar JH. Biostatistical analysis. 4ª Ed. New Jersey: Prentice-Hall; 1999.

Zbinden ZD, Geheber AD, Lehrter RJ, Matthews WJ. Multifaceted assessment of stream fish alpha and beta diversity using spatial models. Hydrobiologia. 2022; 849:1795–820. https://doi.org/10.1007/s10750-022-04824-w

Zeni JO, Pérez-Mayorga MA, Roa–Fuentes CA, Brejão GL, Casatti L. How deforestation drives stream habitat changes and the functional structure of fish assemblages in different tropical regions. Aquat Conserv Mar Freshw Ecosyst. 2019; 29(8):1238–52. https://doi.org/10.1002/aqc.3128

Authors

Izabella Cristina da Silva Penha^1,2 , Naraiana Loureiro Benone³, Ana Júlia Pinheiro da Silva^1,2, Ronaldo Souza da Silva¹, Thaisa Sala Michelan^2,4, Leandro Juen^1,2 and Luciano Fogaça de Assis Montag^1,2

^[1]    Laboratório de Ecologia e Conservação, Instituto de Ciências Biológicas, Universidade Federal do Pará, Rua Augusto Corrêa, 1, 66075-110, Belém, PA, Brazil. (ICSP) engizabellacrista@gmail.com (corresponding author), (AJPS) anajulia_pinheiro@outlook.com, (RSS) ronaldobio.ac@gmail.com, (LJ) leandrojuen@gmail.com, (LFAM) lfamontag@gmail.com.

^[2]    Programa de Pós-graduação em Ecologia, Universidade Federal do Pará (UFPA), Rua Augusto Corrêa, 1, 66075-110, Belém, PA, Brazil.

^[3]    Laboratório de Ecologia Aquática, Unidade Acadêmica Passos, Universidade do Estado de Minas Gerais, Avenida Juca Stockler, 1130, 37900-106, Passos, MG, Brazil. (NLB) nbenone@gmail.com.

^[4]    Laboratório de Ecologia de Produtores Primários, Instituto de Ciências Biológicas, Universidade Federal do Pará, Rua Augusto Corrêa, 1, 66075-110, Belém, PA, Brazil. (TSM) thaisamichelan@gmail.com.

Authors’ Contribution

Izabella Cristina da Silva Penha: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing-original draft, Writing-review and editing.

Naraiana Loureiro Benone: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Visualization, Writing-original draft, Writing-review and editing.

Ana Júlia Pinheiro da Silva: Investigation, Methodology, Writing-review and editing.

Ronaldo Souza da Silva: Conceptualization, Data curation, Formal analysis, Methodology, Writing-original draft, Writing-review and editing.

Thaisa Sala Michelan: Formal analysis, Funding acquisition, Project administration, Resources, Writing-original draft, Writing-review and editing.

Leandro Juen: Formal analysis, Funding acquisition, Project administration, Resources, Visualization, Writing-original draft, Writing-review and editing.

Luciano Fogaça de Assis Montag: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing-original draft, Writing-review and editing.

Ethical Statement​

The procedures for collecting and transporting biological material were authorized by the Instituto Brasileiro de Meio Ambiente e dos Recursos Naturais Renováveis (IBAMA) (ABIO n^o 1267/2020) and the Ethics Committee of the Universidade Federal do Pará (CEUA n^o 8293020418).

Competing Interests

The author declares no competing interests.

Data availability statement

The authors confirm that the data supporting the conclusions of this study are available in the supplementary material. For more detailed information about the data supporting the conclusions of this study, please contact the corresponding author upon reasonable request.

AI statement

The author declares no competing interests.

Funding

The data used in this thesis were obtained to the Hydro Paragominas Company for supporting the research project “Aquatic biota monitoring and assessment upstream and downstream of bauxite pipeline Norsk Hydro Paragominas – Barcarena (Pará, Brazil) – an instream and riverscape approach (process 20/19) through the Biodiversity Research Consortium Brazil-Norway (BRC) and Hydro Alunorte together with Fadesp and for supporting the research project “Avaliação de biota aquática e atributos funcionais de plantas das principais microbacias de Barcarena”, for providing funding and logistical support, as well as for scholarship grants to authors. This paper is number BRC0079 in the publication series of the BRC. We are grateful for the support of the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES – Finance code 001), for awarding the scholarships to ICSP (process 88887848008/2023–00). We thank Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for research productivity scholarships to LJ (process 304710/2019–9), LFAM (process 302881/2022-0) and TSM (process 311835/2023-6).

Supplementary Material

Supplementary material S1

Supplementary material S2

Supplementary material S3

Supplementary material S4

Supplementary material S5

How to cite this article

Penha ICS, Benone NL, Silva AJP, Silva RS, Michelan TS, Juen L, Montag LFA. The ichthyofaunal composition of Amazonian streams reflects localized environmental changes associated with buried mining pipeline infrastructure. Neotrop Ichthyol. 2025; 23(4):e250011. https://doi.org/10.1590/1982-0224-2025-0011

Copyright​

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Distributed under

Creative Commons CC-BY 4.0

© 2025 The Authors.

Diversity and Distributions Published by SBI