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. 2026 Jan 27;108(6):1778–1787. doi: 10.1111/jfb.70335

Feeding ecology of an Amazonian electric knifefish under altered flood‐pulse dynamics caused by hydroelectric damming

Ana F V N M Costa 1,2, Antonio A Jardim Jr 1,3, Erival G Prata 1, Rafael R Gusmão 1, Angelo J Faro Júnior 1, Alan S Fonseca 4,✉, Danielly T Hashiguti 4, Luciano F A Montag 1,2,3, Tiago M S Freitas 2,5
PMCID: PMC13357367  PMID: 41601193

Abstract

This study evaluated the effects of the controlled flood pulse on diet composition, trophic niche breadth and feeding intensity of Archolaemus janeae, an electric knifefish species with a restricted distribution in the Amazon Basin. Monthly samples were collected from December 2020 to November 2021 in the Volta Grande stretch of the Xingu River, and stomach contents of 134 specimens were analysed. The diet was predominantly composed of aquatic insect larvae, with a notable occurrence of terrestrial plant fragments. Hydrological variation affected diet composition, trophic niche breadth and feeding intensity, indicating temporal shifts in resource use associated with changes in river flow. Feeding intensity was higher during filling and flood periods than during the ebb phase, suggesting that the reduction and regulation of the flood pulse by the Belo Monte Hydropower Plant influenced the diet composition by A. janeae. This study provides the first data on the feeding ecology of this species, highlighting the role of habitat heterogeneity in sustaining fish populations and underscoring the potential impacts of hydropower development on the feeding ecology of Amazonian fishes. Such information is essential for the conservation of A. janeae populations in the Xingu River Basin.

Keywords: Archolaemus janeae, diet, Gymnotiformes, hydroelectric, Xingu River

1. INTRODUCTION

The flood pulse is a seasonal hydrological phenomenon characteristic of floodplain forest areas, such as the várzeas of the Amazon basin, and plays a fundamental role in the ecological dynamics of these ecosystems (Freitas et al., 2011; Junk et al., 1989). During the high‐water period, rising water levels inundate forests adjacent to the main river channel, thereby increasing connectivity between aquatic and terrestrial habitats (Goulding, 1980; Junk et al., 1989). This spatial expansion creates a diversity of habitats that provide foraging opportunities, reproductive sites and shelter for numerous fish species (Junk et al., 1989; Lowe‐McConnell, 1999). Conversely, during the dry season, when water levels recede, fish concentrate and become restricted to habitats such as river channels and rapids (Barbosa et al., 2018; Goulding, 1980).

This hydrological dynamic strongly influences the feeding ecology of fishes: during high‐water periods, the input of allochthonous resources (e.g. plant materials, terrestrial invertebrates and detritus) increases, integrating terrestrial production into aquatic food webs (Correa & Winemiller, 2014; Dary et al., 2017; Freitas et al., 2022). In contrast, during low‐water periods, the reduction of terrestrial‐derived inputs leads to a diet predominantly based on autochthonous resources, such as organic matter, algae and aquatic macroinvertebrates (Cunico et al., 2002).

Given this strong spatial–temporal variation in resource availability, assessing fish trophic ecology through stomach content analysis represents a valuable approach to understanding feeding interactions between species and their environment (Amundsen & Sánchez‐Hernández, 2019). This method allows direct identification of ingested items, enabling the trophic classification, qualitative and quantitative estimation of diet composition, analysis of dietary niche breadth and detection of seasonal shift in resource use (Bianchi‐Costa et al., 2023). In systems influenced by the flood pulse, stomach content analysis reveals how fish diets respond to fluctuations in water levels, reflecting changes in the availability of autochthonous and allochthonous resources across hydrological periods (Penha et al., 2024, 2025; Prata et al., 2025). It also provides critical insights into the ecological role of each species within the food web (Correa & Winemiller, 2018; Pereyra et al., 2021).

However, this ecological balance can be disrupted by anthropogenic interventions associated with hydropower development, which alter the natural flow regimes, reduce habitat connectivity and modify resource availability, thereby directly impacting the trophic ecology of local fish assemblages (Barros et al., 2020; Seabra et al., 2025; Timpe & Kaplan, 2017). A notable case is the Belo Monte Hydroelectric Power Plant (UHE Belo Monte) in the Volta Grande do Xingu (VGX), Xingu River basin (Brazil). Despite modifications to its original design aimed at mitigating social and environmental impacts by adopting a run‐of‐river operation (Baird et al., 2025), the plant has profoundly altered the hydrological regime, reducing downstream discharge, negatively affecting the flood pulse, fishery yields and the ecological integrity of aquatic biota (Castro‐Diaz et al., 2018; Stickler et al., 2013). In this context, studies of fish feeding ecology are essential because they allow assessment of the ecological consequences of river damming, thereby supporting conservation and management strategies for impacted species (Penha et al., 2024, 2025; Prata et al., 2025).

Among the ichthyofauna of the Xingu River, the electric knifefish Archolaemus janeae (Vari et al., 2012) stands out. This species, with a restricted distribution in the Amazon basin (upper Tapajós River and middle Xingu River), belongs to the family Sternopygidae (order Gymnotiformes) (Rodrigues et al., 2021; Vari et al., 2012). It exhibits an elongated, hydrodynamic body adapted for swimming in freshwater environments, particularly over benthic substrates (Vari et al., 2012), with an average total length of 38 cm in Xingu River populations (Giarrizzo et al., 2015). Despite its restricted range and distinctive morphology, the ecology and feeding habits of A. janeae remain poorly studied. Preliminary observations suggest a carnivorous diet, feeding on aquatic insect larvae, particularly those of Chironomidae (Vari et al., 2012). This pattern is consistent with other Sternopygidae species, which exhibit benthivorous and insectivorous diets, highlighting their role in structuring food webs and contributing to the ecological dynamics of Amazonian aquatic systems (Campos et al., 2021). As an intermediate consumer in benthic food webs, A. janeae may play an important role in regulating macroinvertebrate populations and transferring energy across trophic levels (Giora et al., 2014).

Beyond their morphological features, these gymnotiform fishes possess specialized electrogenic adaptations used for communication and prey detection in low‐visibility environments (Nanjappa et al., 2000). The selection of A. janeae as a model species in this study is justified by the scarcity of ecological information available on its feeding ecology, particularly in areas subject to severe environmental alterations such as those caused by river damming. Thus, the evaluation of trophic metrics provides an opportunity to infer how changes in the flood pulse affect trophic plasticity in a controlled‐flow system (Abujanra et al., 2009; Bianchi‐Costa et al., 2023).

Accordingly, this study aimed to assess the effects of the flood pulse on the trophic ecology of Archolaemus janeae in the Volta Grande region of the middle Xingu River, within a reduced‐flow stretch directly impacted by the Belo Monte Hydropower Plant. Specifically, we sought to evaluate how flow regulation influences diet composition, trophic niche breadth and feeding intensity in this species. We hypothesize that variations in hydrological regimes alter the availability and diversity of food resources, leading to a broader trophic niche and greater feeding intensity during high‐water periods due to increased inputs of allochthonous items (Bianchi‐Costa et al., 2023; Silva et al., 2021). Conversely, during low‐water periods, when resource availability is more limited, we expected lower diet diversity, reduced niche breadth and lower feeding intensity, reflecting a predominance of autochthonous items (Petry et al., 2003).

2. MATERIALS AND METHODS

2.1. Study area

The study was conducted in the reduced flow stretch (RFS) of the Belo Monte Hydropower Plant, located in the Volta Grande do Xingu, middle Xingu River (Figure 1). This section corresponds to the stretch between the Pimental and the Belo Monte dams and has been characterized by a controlled hydrological regime since the implementation of this hydropower complex in 2016 (Bertassoli Jr et al., 2021).

FIGURE 1.

FIGURE 1

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Sampling sites of Archolaemus janeae in the reduced‐flow stretch of the Volta Grande do Xingu, middle Xingu River (Pará, Brazil).

The region has a hot and humid tropical climate, classified as Am under the Köppen‐Geiger system (Peel et al., 2007), with a mean annual temperature ranging from 17.5 to 24.5°C and relative humidity between 84% and 86%. Annual precipitation varies from 2066 to 2379 mm, with a pronounced rainy season from December to May (Barbosa et al., 2018; Sabaj et al., 2024). The physicochemical characteristics of the Xingu River within the RFS include clear waters with transparency ranging from 1 to 5 m, rocky outcrops, predominantly sandy substrates and Amazonian forest interspersed with open areas (Barbosa et al., 2018; Fitzgerald et al., 2018).

Before the dams' construction, the Xingu River exhibited a natural flood pulse with well‐defined seasonal variations between flood and dry periods, which were fundamental for sustaining local biodiversity and ecological processes (Sabaj et al., 2024). Since the dams' operation, the RFS has been subject to artificial flow regulation, with discharge reduction of up to 80% below the historical annual average recorded for the Volta Grande do Xingu (Penha et al., 2025; Zuanon et al., 2019).

Hydrological records indicated marked seasonal fluctuations during the study, with mean daily discharges ranging from 8215.9 m3 s−1 in March (flood peak) to 707.8 m3 s−1 in October (dry season minimum). Following this seasonal regime, we distinguished four distinct hydrological periods: filling (December to February), flood (March to May), ebb (June to August) and dry seasons (September to November) (Freitas et al., 2022; Lima et al., 2023; Penha et al., 2024). In this study, hydrological periods were adopted as a proxy for the flood pulse, with the rising and flood stages corresponding to peak water levels, and the receding and dry stages reflecting the lowest discharges.

2.2. Sampling and processing of biological material

Sampling was conducted monthly between December 2020 and November 2021 in backwater habitats. Fish were captured using rectangular gillnets, 20 m long and 1 m high, made of monofilament nylon line with mesh sizes of 2, 3, 4, 5, 6, 7, 8, 10, 12, 16 and 18 cm between opposite knots. Three sets of nets were deployed daily and left in the water for 4 h (from 17:00 to 21:00), coinciding with the species' activity both during the day and at night (Vari et al., 2012).

Captured specimens were anaesthetised with benzocaine (0.1 g L−1) and subsequently euthanized following the guidelines of the National Council for the Control of Animal Experimentation. Standard length (SL, cm) and total mass (TM, g) were recorded for each specimen. Stomachs were removed through a longitudinal incision from the urogenital opening to the base of the head, preserved in 70% ethanol and transported to the Laboratory of Ecology and Conservation at the Federal University of Pará (UFPA).

Voucher specimens were fixed in 10% formalin for 48 h, transferred to 70% ethanol and incorporated into the ichthyological collection of the Zoological Museum at UFPA. All procedures for capture, collection and transport of biological material were authorized by the Brazilian Institute of Environment and Renewable Natural Resources (SISBIO permit No. 1267/2020) and approved by the Animal Use Ethics Committee of UFPA (CEUA No. 8.293.020.418).

In the laboratory, stomach contents of A. janeae were analysed under a stereomicroscope with up to 32× magnification. Food items were identified to the lowest possible taxonomic level using specialized literature (Buckup, 2007; Hamada et al., 2014; Mugnai, 2010) and through consultation with experts. Each food item was weighed individually on a precision electronic balance (0.0001 g). Items were subsequently grouped into five trophic categories: aquatic insects, fishes, inorganic detritus (autochthonous), terrestrial insects and terrestrial plants (allochthonous).

2.3. Data analysis

A total of 177 A. janeae specimens were captured, with standard lengths ranging from 13.5 to 40 cm (mean ± standard deviation = 25.1 ± 4.5 cm) and weights between 8.7 and 68 g (20.5 ± 9.1 g). Of these, 57 individuals had empty stomachs, while the remaining 120 were used for analyses of diet composition, niche breadth and feeding intensity. Diet composition was assessed using frequency of occurrence (FOi%) and percentage of mass (Mi%), where FOi% represents the proportion of stomachs containing a given food item relative to the total number of stomachs analysed (Hyslop, 1980), while Mi% represents the mass contribution (g) of each food item relative to the total stomach contents mass (Hynes, 1950). Both parameters were combined to calculate the alimentary index (Ai%) following the modified formula of Kawakami and Vazzoler (1980): Ai% = [(FOi% × Mi%)/(∑FOi% × Mi%)] × 100, where Ai% indicates the relative importance of each food item by simultaneously integrating occurrence and mass, providing a more robust representation of the main resources used by the species. The Ai% was calculated for the entire sample and separately for each hydrological period. To test for differences in diet composition among hydrological periods, we applied permutational multivariate analysis of variance (PERMANOVA) using the adonis2 function from the vegan package (Oksanen et al., 2022) in RStudio (R Core Team, 2025). In this analysis, Ai% values grouped by collection day within each hydrological period were considered as sampling units.

We applied a non‐metric multidimensional scaling (NMDS) ordination based on the Bray–Curtis dissimilarity index. The NMDS was conducted with two dimensions (k = 2) and the ordination quality was assessed using the stress value, which measures the degree of correspondence between the original dissimilarities and their representation in the reduced dimensional space. The resulting ordination was visualized in a scatterplot with 95% confidence ellipses for each hydrological period.

Trophic niche breadth across hydrological periods was evaluated using permutational analysis of multivariate dispersion (PERMDISP) with the vegdist and betadisper functions, also from the vegan package. This method estimates the average distance of individuals to the group centroid (i.e. hydrological period), reflecting within‐group dietary variation and thus the population's trophic niche breadth (Correa & Winemiller, 2014). For both PERMANOVA and PERMDISP, pairwise tests were conducted when significant differences were detected in the global models (Zar, 2010).

Feeding intensity was assessed using the repletion index (RI), calculated for each individual following Zavala‐Camin (1996): RI = (MI/Mt) × 100, where MI is the stomach contents mass and Mt is the total body mass. These values were tested for normality and homoscedasticity; since assumptions were not met, a nonparametric statistical approach was adopted. Variation in RI among hydrological periods was tested using the Kruskal–Wallis test (kruskal. test function, stats package, RStudio). When significant differences were found, post hoc pairwise multiple comparisons were performed using Dunn's test to identify which periods differed in feeding intensity. For all statistical analyses, the significance level was set at Inline graphic = 0.05 (Zar, 2010).

3. RESULTS

The diet of the A. janeae population comprised 14 food items, with the most important being fragments of terrestrial plants (33% Ai), followed by fragments of terrestrial insects (22% Ai) and Odonata larvae (Gomphidae) (14% Ai). Detailed information on the food items and their relative importance in each hydrological period is presented in Table 1. The most representative trophic categories were aquatic insects (40% Ai), followed by terrestrial plants (33% Ai) and terrestrial insects (22% Ai). The remaining categories together accounted for only 5% (Ai%; Figure 2).

TABLE 1.

Alimentary index (Ai%) of food items and trophic categories in the diet of Archolaemus janeae collected monthly and across different hydrological periods in the middle Xingu River, eastern Amazonia, Brazil.

Foods items 2020 2021 IAi%
Filling Flood Ebb Dry
Dec, n = 2 Jan, n = 2 Feb, n = 3 Mar, n = 2 Apr, n = 3 May, n = 3 Jun, n = 13 Jul, n = 15 Aug, n = 29 Sep, n = 29 Oct, n = 5 Nov, n = 14
Autochthonous
Aquatic insect 0.40
Ephemeroptera 0.01 0.15 <0.01 0.03 0.01 0.03 <0.01 <0.01
Leptophlebiidae <0.01 0.30 0.01 <0.01 <0.01 <0.01 <0.01
Odonata larvae 0.28 0.01 0.19 0.02 <0.01 0.02 0.13
Libellulidae <0.01 <0.01 0.08
Gomphidae <0.01 <0.01 0.25 0.66 0.09 0.29 0.13 0.16 0.01 0.06
Trichoptera <0.01 0.01 0.02 0.40 0.01
Diptera 0.02 <0.01 0.16 <0.01 <0.01 0.17 0.25 0.25 0.51 0.06
Diptera pulp <0.01 <0.01 <0.01
Belostomatidae 0.01
Perlidae <0.01
Fish <0.01
Fish scale <0.01 <0.01
Inorganic debris 0.05
Sediment 0.09 <0.01 <0.01 <0.01 <0.01 <0.01 0.43 <0.01 0.02 <0.01 0.06
Allochthonous
Terrestrial insect 0.22
Insect fragment 0.14 0.02 0.05 0.22 0.03 0.89 0.06 0.18 0.49 0.11 0.47 0.04
Terrestrial plant 0.33
Plant fragments 0.84 0.59 0.65 0.53 <0.01 0.01 0.46 0.05 0.05 0.18 0.56
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Note: n = number of stomachs containing food items.

FIGURE 2.

FIGURE 2

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Alimentary index (Ai%) of the diet of Archolaemus janeae across different hydrological periods in the middle Xingu River, eastern Amazonia, Brazil. *Represents values <3% of contribution.

The NMDS ordination showed good fit (stress = 0.12), with substantial overlap but clear trends of separation among hydrological periods (Figure 3). Accordingly, we observed significant differences in the diet composition of A. janeae across hydrological periods (PERMANOVA; pseudo‐F = 1.60, df = 3, p = 0.01) (Table 2). Pairwise comparisons indicated differences between the filling and ebb periods (p = 0.04) and between the flood and ebb periods (p = 0.03). The most important items for each period were terrestrial plants (79%), predominantly during the filling period, terrestrial insects (IAi = 69%) during the flood period and aquatic insects during the ebb and dry periods (IAi = 47% and IAi = 56%, respectively).

FIGURE 3.

FIGURE 3

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Non‐metric multidimensional scaling (NMDS) ordination showing diet composition of Archolaemus janeae across hydrological periods in the Xingu River. Each point represents a sample and ellipses indicate 95% confidence intervals around group centroids for each period. The two‐dimensional NMDS solution yielded a stress value of 0.12, indicating a satisfactory representation of Bray–Curtis dissimilarities.

TABLE 2.

Results of statistical analyses comparing the diet composition (PERMANOVA) and trophic niche breadth (PERMDISP) of Archolaemus janeae across different hydrological periods in the middle Xingu River, eastern Amazonia, Brazil.

Hydrological periods PERMANOVA PERMIDISP
Pseudo‐F = 1.60, p = 0.01 F = 5.12, p = <0.01
F value p value t value p value
Filling vs. flood 1.2 0.27 0.02 0.87
Filling vs. ebb 1.77 0.04* 5.4 0.02*
Filling vs. dry 1.33 0.12 9.84 0.01*
Flood vs. ebb 1.87 0.03* 6.33 0.01*
Flood vs. dry 1.47 0.08 9.48 0.01*
Ebb vs. dry 1.59 0.06 0.72 0.37
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Note: * indicates statistically significant differences (p < 0.05).

The effect of the flood pulse on trophic niche breadth among hydrological periods was also detected (PERMDISP; F = 5.12, df = 3, p < 0.01) (Table 2). Differences were evident when comparing the mean centroid distances to for the flood (centroid distance; dc = 0.58) with those for the ebb (0.65, p = 0.02) and dry (0.66, p < 0.01), as well as between filling (dc = 0.59) and both ebb (p = 0.02) and dry (p < 0.01) (Figure 4). These results suggest a generalist feeding pattern for the population, with the exploitation of different resources across hydrological periods.

FIGURE 4.

FIGURE 4

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Trophic niche breadth of Archolaemus janeae across hydrological periods in the reduced‐flow stretch of the middle Xingu River. The horizontal line inside each box represents the median, the box boundaries indicate the first and third quartiles, and the whiskers show the minimum and maximum values.

Feeding intensity, represented by the RI, ranged from 0.001 to 0.86 (0.11 ± 0.18). However, no significant differences were observed in the amount of food ingested by A. janeae specimens among hydrological periods (Kruskal–Wallis; χ 2 = 7.92, df = 3, p = 0.06).

4. DISCUSSION

The stomach content analysis of the Archolaemus janeae population revealed a diet predominantly composed of aquatic insect larvae, confirming an insectivorous feeding habit. Additionally, fragments of terrestrial plants were recorded, indicating the opportunistic use of allochthonous resources available in the environment. Diet composition and trophic niche breadth varied across hydrological periods, supporting our hypothesis and suggesting that the species adjusts its feeding spectrum in response to seasonal changes in the flood pulse. On the other hand, mean feeding intensity remained constant across periods, contrary to the expectation of greater consumption during low‐water conditions. This pattern may reflect a stable foraging strategy or structural constraints of the environment resulting from the flow regulation imposed by the Belo Monte Hydropower Plant, which may dampen the natural variation in resource availability typically observed under unregulated flood pulse conditions (Arantes et al., 2019). Overall, these findings indicate a relatively constant quantitative consumption pattern across hydrological periods, possibly as a response to hydrological alteration.

The dietary composition of A. janeae across hydrological periods reveals a trophic strategy characterized by high flexibility and responsiveness to spatial and temporal variation in resource availability (Ferreira et al., 2024; Winemiller, 1996), a pattern previously described for floodplain fishes (Lima et al., 2023; Mérona & Rankin‐de‐Mérona, 2004; Röpke et al., 2014). The predominance of allochthonous resources during the filling and flood periods, such as plant material and terrestrial insects, reinforces the role of flooded forests as key resource providers for marginal‐habitat species, as documented for fishes from Amazonian floodplains and other tropical flooded forests (Correa & Winemiller, 2018; Duarte et al., 2019; Rayner et al., 2009). For example, benthic species from the Purus River, including gymnotiform fish, consumed large amounts of allochthonous material during high water, reflecting the high availability of these resources and strengthening the trophic linkage between aquatic fauna and the riparian zone in floodplain environments (Correa & Winemiller, 2018; Junk, 2005).

During the ebb and dry periods, the increased consumption of aquatic insects appears to be associated with the higher concentration of benthic prey (Rayner et al., 2009). The greater intake of aquatic insects during water‐level decline represents a recurrent trophic response under different hydrological contexts in the Amazon Basin (Duarte et al., 2019; Oliveira et al., 2025; Zuanon et al., 2019). Similar patterns have also been reported in tropical systems outside the Amazon, such as the Mulgrave River in Australia, where benthic fishes exhibited high sensitivity to the availability of invertebrates across hydrological periods (Rayner et al., 2009). In that study, despite a marked reduction in the ingestion of benthic invertebrates during high water, fish displayed trophic adjustments and spatial displacements to maximize access to preferred resources, highlighting dietary plasticity as an adaptive mechanism in environments subject to pronounced seasonal fluctuations in prey availability (Pyke & Starr, 2021; Rayner et al., 2009).

The presence of terrestrial plant material, commonly recorded in the stomach contents of floodplain fishes, demonstrates the species' capacity to exploit resources derived from increased lateral river connectivity (Correa & Winemiller, 2018; Junk, 2005). The expansion of the feeding spectrum, even with items of lower energetic value, represents an adaptive response to the sudden increase in resource diversity and abundance during the flood phase (MacArthur & Pianka, 1966; Pyke & Starr, 2021). Moreover, given the spatial heterogeneity characteristic of riverine environments (Woodward & Hildrew, 2002), the incorporation of resources of different origins may reflect a flexible response to the rapid shifts in food availability promoted by hydrological dynamics (Barbosa et al., 2018; Freitas et al., 2011, 2022). This pattern likely resulted in the expansion of niche breadth during high and receding water, indicating opportunistic and diversified resource exploitation, and reflecting the species' capacity to broaden its feeding spectrum under seasonal environmental variation (Blanchette et al., 2014; Cardoso & Couceiro, 2017; Medeiros & Arthington, 2014).

The absence of a flood pulse effect on feeding intensity suggests a flexible foraging strategy that maintains relatively constant food intake across hydrological periods. This pattern may represent an adaptation typical of species inhabiting strongly seasonal environments, where sustaining a minimum continuous intake is essential to meet energetic and physiological demands regardless of fluctuation in resource (Dill, 1983; O'Gorman et al., 2021). From the perspective of optimal foraging theory (MacArthur & Pianka, 1966), stability in feeding intensity may indicate that A. janeae adjusts its selectivity and foraging effort according to resource abundance and accessibility, exploiting more profitable prey when available and resorting to less preferred items during periods of scarcity, particularly in hydrologically dynamic environments (Blanchette et al., 2014; Pyke & Starr, 2021; Röpke et al., 2014).

Alterations in the hydrological regime caused by large dams, such as the Belo Monte Hydropower Plant in the Volta Grande do Xingu, represent a direct threat to the ecological processes sustaining fish feeding (Penha et al., 2025; Prata et al., 2025; Seabra et al., 2025). Studies conducted in the Amazon and elsewhere have demonstrated that flow regulation directly alters fish feeding patterns (Baird et al., 2025). Common responses include reduced prey diversity and availability, diet simplification and increased dietary generalism as compensatory mechanisms for resource loss (Luo et al., 2024; Ru et al., 2022; Wang et al., 2016). These dietary shifts are directly linked to the physical modification of riverine habitats, as decreased connectivity between the main channel and floodplains, combined with reduced amplitude and duration of flooding, limits access to marginal habitats rich in aquatic invertebrates and allochthonous material, key dietary components for many fish species (Agostinho et al., 2008).

The consequences of hydrological alteration are even more critical for species with restricted distributions, such as A. janeae (Rodrigues et al., 2021; Vari et al., 2012). The spatial limitation imposed by its narrow geographic range, combined with the changes in flow regime resulting from the Belo Monte Dam, exposes this population to less favourable and less predictable environmental conditions (Agostinho et al., 2004; Fitzgerald et al., 2018). This scenario forces individuals to adopt more generalist diets, as observed in our results, reflecting trophic plasticity that, while ensuring short‐term intake, may incur energetic and ecological costs in the medium and long term (Costa et al., 2024; Lima et al., 2018). For a restricted species whose persistence already depends on specific environmental conditions, the loss of these key processes may represent an additional threat to population viability (Agostinho et al., 2004; Petsch et al., 2023).

Our results reinforce that flood pulse alteration by dams such as Belo Monte exerts pressures that go far beyond physical habitat modification. By compromising the dynamics of food resource availability, flow regulation may generate physiological consequences for fish populations, especially those with restricted distributions, such as Archolaemus janeae. The loss of trophic diversity, the reduced quality and predictability of resources, and the need for forced dietary adjustments reveal a scenario of ecological vulnerability with potential cumulative effects on survival and recruitment. These findings highlight the urgent need to integrate the maintenance of ecologically adequate flow regimes into hydropower management policies. Strategies such as the implementation of environmental flows and continuous monitoring of fish trophic responses should be prioritized to mitigate the negative impacts of flow regulation and preserve the ecological functionality of endemic and range‐restricted species in rivers undergoing intense hydrological modification.

AUTHOR CONTRIBUTIONS

Conceptualization: A.F.V.N.M.C., A.A.J.J., R.R.G., A.S.F., T.M.S.F. and L.F.A.M. Data collection: A.F.V.N.M.C., A.A.J.J., R.R.G., E.G.P. and T.M.S.F. Data analysis: A.A.J.J. and E.G.P. Writing – original draft: A.F.V.N.M.C., A.A.J.J., E.G.P., A.J.F.J. and A.S.F. Writing – review and editing: D.T.H., L.F.A.M. and T.M.S.F. Funding acquisition and supervision: L.F.A.M.

FUNDING INFORMATION

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

CONFLICT OF INTEREST STATEMENT

The authors declare there are no competing interests.

ACKNOWLEDGEMENTS

The authors thank the National Council for Scientific and Technological Development for the research productivity grant awarded to L.F.A.M. (Process No. 302881/2022‐1) and for the Industrial Technological Development–B grant awarded to A.S.F. (Process No. 382488/2024‐5). We also thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior‐Brasil for the doctoral scholarship granted to A.F.V.N.M.C. (Process No. 88887.176348/2025‐00) and A.A.J.J. (Process No. 88887.687748/2022‐00). A.J.F.J. thank BRC Norsk Hydro for their scientific initiation scholarship and the ichthyology team of the Laboratório de Ecologia e Conservação at the Universidade Federal do Pará for their support.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

REFERENCES

  1. Abujanra, F. , Agostinho, A. A. , & Hahn, N. S. (2009). Influência do regime de cheias na condição corporal de peixes de diferentes guildas tróficas na planície de inundação do Alto Rio Paraná, Brazil. Brazilian Journal of Biology, 69, 469–479. 10.1590/S1519-69842009000300003 [DOI] [PubMed] [Google Scholar]
  2. Agostinho, A. A. , Gomes, L. C. , Veríssimo, S. , & Okada, E. K. (2004). Flood regime, dam regulation and fish in the upper Paraná River: Effects on assemblage attributes, reproduction and recruitment. Reviews in Fish Biology and Fisheries, 14, 11–19. 10.1007/s11160-004-3551-y [DOI] [Google Scholar]
  3. Agostinho, A. A. , Pelicice, F. M. , & Gomes, L. C. (2008). Dams and the fish fauna of the neotropical region: Impacts and management related to diversity and fisheries. Brazilian Journal of Biology, 68, 1119–1132. 10.1590/S1519-69842008000500019 [DOI] [PubMed] [Google Scholar]
  4. Amundsen, P. A. , & Sánchez‐Hernández, J. (2019). Feeding studies take guts: Critical review and recommendations of methods for stomach contents analysis in fish. Journal of Fish Biology, 95(6), 1364–1373. 10.1111/jfb.14151 [DOI] [PubMed] [Google Scholar]
  5. Arantes, C. C. , Freeman, M. C. , Leitão, R. P. , Zuanon, J. , Espírito‐Santo, H. M. V. , Barreto, D. S. , & Castelo, R. A. (2019). Effects of hydropower on fish and fisheries in the Amazon, Orinoco, and Tocantins rivers. Frontiers in Ecology and the Environment, 17(9), 475–483. 10.1002/fee.2110 [DOI] [Google Scholar]
  6. Baird, I. G. , Ziegler, A. D. , Fearnside, P. M. , Pineda, A. , Sasges, G. , Strube, J. , Thomas, K. A. , Schmutz, S. , Greimel, F. , & Hayes, D. S. (2025). Ruin‐of‐the‐rivers? A global review of run‐of‐the‐river dams. Environmental Management, 75(2), 175–190. 10.1007/s00267-024-02062-5 [DOI] [PubMed] [Google Scholar]
  7. Barbosa, T. A. , Rosa, D. C. , Soares, B. E. , Costa, C. H. , Esposito, M. C. , & Montag, L. F. (2018). Effect of flood pulses on the trophic ecology of four piscivorous fishes from the eastern Amazon. Journal of Fish Biology, 93(1), 30–39. 10.1111/jfb.13669 [DOI] [PubMed] [Google Scholar]
  8. Barros, K. D. N. , Brabo, M. F. , & Ferreira, A. C. (2020). Impactos de usinas hidrelétricas sobre os recursos pesqueiros amazônicos: Os casos de Tucuruí e Belo Monte. Gaia Scientia, 14(4), 198–1268. 10.22478/ufpb.1981-1268.2020v14n4.51868 [DOI] [Google Scholar]
  9. Bertassoli, D. J., Jr. , Sawakuchi, H. O. , de Araújo, K. R. , Camargo, M. G. , Alem, V. A. , Pereira, T. S. , Krusche, A. V. , Bastviken, D. , Richey, J. E. , & Sawakuchi, A. O. (2021). How green can Amazon hydropower be? Net carbon emission from the largest hydropower plant in Amazonia. Science Advances, 7(26), eabe1470. 10.1126/sciadv.abe1470 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bianchi‐Costa, I. C. , Quirino, B. A. , Cardozo, A. L. P. , Yofukuji, K. Y. , Aleixo, M. H. F. , & Fugi, R. (2023). Water‐level fluctuations lead to changes in the diet of an omnivorous fish in a floodplain. Neotropical Ichthyology, 21(1), e220064. 10.1590/1982-0224-2022-0064 [DOI] [Google Scholar]
  11. Blanchette, M. L. , Davis, A. M. , Jardine, T. D. , & Pearson, R. G. (2014). Omnivory and opportunism characterize food webs in a large dry‐tropics river system. Freshwater Science, 33(1), 142–158. 10.1086/674632 [DOI] [Google Scholar]
  12. Buckup, P. A. (2007). Catálogo das espécies de peixes de água doce do Brasil. Museu Nacional. [Google Scholar]
  13. Campos, W. A. C. , Vieira, T. S. G. , & Yamamoto, K. C. (2021). Dieta de sarapós da família Sternopygidae em ambientes de praia no Rio Negro, Amazonas. Revista Ibero‐Americana de Ciências Ambientais, 12(2), 482–492. 10.6008/CBPC2179-6858.2021.002.0041 [DOI] [Google Scholar]
  14. Cardoso, A. , & Couceiro, S. (2017). Insects in the diet of fish from amazonian streams, in western Pará, Brazil. Marine and Freshwater Research, 68, 2052–2060. 10.1071/MF16173 [DOI] [Google Scholar]
  15. Castro‐Diaz, L. , Lopez, M. C. , & Moran, E. (2018). Gender‐differentiated impacts of the Belo Monte hydroelectric dam on downstream fishers in the Brazilian Amazon. Human Ecology, 46(3), 411–422. 10.1007/s10745-018-9992-z [DOI] [Google Scholar]
  16. Correa, S. B. , & Winemiller, K. (2018). Terrestrial–aquatic trophic linkages support fish production in a tropical oligotrophic river. Oecologia, 186(4), 1069–1078. 10.1007/s00442-018-4093-7 [DOI] [PubMed] [Google Scholar]
  17. Correa, S. B. , & Winemiller, K. O. (2014). Niche partitioning among frugivorous fishes in response to fluctuating resources in the Amazonian floodplain forest. Ecology, 95(1), 210–224. 10.1890/13-0393.1 [DOI] [PubMed] [Google Scholar]
  18. Costa, G. , Weiss, B. , & Piedade, M. T. F. (2024). Responses of trophic fish guilds upstream and downstream of the Balbina dam, Central Amazon, northern Brazil. Neotropical Ichthyology, 22(2), e230009. 10.1590/1982-0224-2023-0009 [DOI] [Google Scholar]
  19. Cunico, A. M. , Graça, W. D. , Veríssimo, S. , & Bini, L. M. (2002). Influência do nível hidrológico sobre a assembléia de peixes em lagoa sazonalmente isolada da planície de inundação do alto rio Paraná. Acta Scientiarum. Biological Sciences, 24(2), 383–389. [Google Scholar]
  20. Dary, E. P. , Ferreira, E. , Zuanon, J. , & Röpke, C. P. (2017). Diet and trophic structure of the fish assemblage in the mid‐course of the Teles Pires River, Tapajós River basin, Brazil. Neotropical Ichthyology, 15, e160173. 10.1590/1982-0224-20160173 [DOI] [Google Scholar]
  21. Dill, L. (1983). Adaptive flexibility in the foraging behavior of fishes. Canadian Journal of Fisheries and Aquatic Sciences, 40, 398–408. 10.1139/F83-058 [DOI] [Google Scholar]
  22. Duarte, C. , Magurran, A. E. , Zuanon, J. , & Deus, C. P. (2019). Trophic ecology of benthic fish assemblages in a lowland river in the Brazilian Amazon. Aquatic Ecology, 53(4), 707–718. [Google Scholar]
  23. Ferreira, V. M. B. , Soares, B. E. , Leal, J. S. , & Albrecht, M. P. (2024). Spatial factors overcome seasonality regulating the consumption of allochthonous food resources by fishes from tropical lotic ecosystems. Freshwater Biology, 69(6), 783–791. 10.1111/fwb.14245 [DOI] [Google Scholar]
  24. Fitzgerald, D. B. , Sabaj Perez, M. H. , Sousa, L. M. , Gonçalves, A. P. , Py‐Daniel, L. R. , Lujan, N. K. , Zuanon, J. , Winemiller, K. O. , & Lundberg, J. G. (2018). Diversity and community structure of rapids‐dwelling fishes of the Xingu River: Implications for conservation amid large‐scale hydroelectric development. Biological Conservation, 222, 104–112. 10.1016/j.biocon.2018.04.002 [DOI] [Google Scholar]
  25. Freitas, T. M. S. , Almeida, V. H. D. C. , Valente, R. D. M. , & Montag, L. F. A. (2011). Feeding ecology of Auchenipterichthys longimanus (Siluriformes: Auchenipteridae) in a riparian flooded forest of eastern Amazonia, Brazil. Neotropical Ichthyology, 9, 629–636. 10.1590/S1679-62252011005000032 [DOI] [Google Scholar]
  26. Freitas, T. M. S. , Prudente, B. D. S. , & Montag, L. F. A. (2022). Flood pulse influence on the feeding ecology of two amazonian auchenipterid catfishes. Neotropical Ichthyology, 20(1), e210103. 10.1590/1982-0224-2021-0103 [DOI] [Google Scholar]
  27. Giarrizzo, T. , de Sena Oliveira, R. R. , Costa Andrade, M. , Pedrosa Gonçalves, A. , Barbosa, T. A. P. , Martins, A. R. , Marques, D. K. , dos Brito Santos, J. L. , de Paula da Silva Frois, R. , de Oliveira Albuquerque, T. P. , Fogaça Assis Montag, L. , Camargo, M. , & de Melo Sousa, L. (2015). Length–weight and length–length relationships for 135 fish species from the Xingu River (Amazon Basin, Brazil). Journal of Applied Ichthyology, 31(2), 415–424. 10.1111/jai.12677 [DOI] [Google Scholar]
  28. Giora, J. , Tarasconi, H. M. , & Fialho, C. B. (2014). Reproduction and feeding of the electric fish Brachyhypopomus gauderio (Gymnotiformes: Hypopomidae) and the discussion of a life history pattern for gymnotiforms from high latitudes. PLoS One, 9(9), e106515. 10.1371/journal.pone.0106515 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Goulding, M. (1980). The fishes and the forest: Explorations in amazonian natural history. University of California Press. [Google Scholar]
  30. Hamada, N. , Nessimian, J. , & Querino, R. (2014). Insetos aquáticos na Amazônia brasileira: Taxonomia, biologia e ecologia. INPA. [Google Scholar]
  31. Hynes, H. B. N. (1950). The food of fresh‐water sticklebacks (Gasterosteus aculeatus and Pygosteus pungitius), with a review of methods used in studies of the food of fishes. The Journal of Animal Ecology, 19, 36–58. https://www.jstor.org/stable/1570 [Google Scholar]
  32. Hyslop, E. J. (1980). Stomach contents analysis – A review of methods and their application. Journal of Fish Biology, 17, 411–429. 10.1111/j.1095-8649.1980.tb02775.x. [DOI] [Google Scholar]
  33. Junk, W. J. (2005). Flood pulsing and the linkages between terrestrial, aquatic, and wetland systems. SIL Proceedings, 1922‐2010, 29(1), 11–38. 10.1080/03680770.2005.11901972 [DOI] [Google Scholar]
  34. Junk, W. J. , Bayley, P. B. , & Sparks, R. E. (1989). The flood pulse concept in river‐floodplain systems. Canadian Special Publication of Fisheries and Aquatic Sciences, 106, 110–127. [Google Scholar]
  35. Kawakami, E. , & Vazzoler, G. (1980). Método gráfico e estimativa de índice alimentar aplicado no estudo de alimentação de peixes. Boletim Do Instituto Oceanográfico, 29, 205–207. 10.1590/S0373-55241980000200043. [DOI] [Google Scholar]
  36. Lima, F. P. D. , Nobile, A. B. , Freitas‐Souza, D. , Carvalho, E. D. , & Vidotto‐Magnoni, A. P. (2018). Can dams affect the trophic structure of ichthyofauna? Long‐term effects in the neotropical region. Iheringia, Série Zoologia, 108, e2018030. 10.1590/1678-4766e2018030 [DOI] [Google Scholar]
  37. Lima, M. D. S. , Martins, S. S. , Montag, L. F. A. , & Freitas, T. M. S. (2023). Dietary shift of a pimelodid catfish in response to the flood pulse in the Xingu River. Neotropical Ichthyology, 21(4), e230097. 10.1590/1982-0224-2023-0097 [DOI] [Google Scholar]
  38. Lowe‐McConnell, R. H. (1999). Estudos ecológicos de comunidades de peixes tropicais. EDUSP. [Google Scholar]
  39. Luo, S. , Wang, Z. , Zhang, S. , Mu, H. , Jiao, Y. , Qu, X. , Wang, Q. , Yang, R. , Zuo, Y. , & Jin, S. (2024). Formation of adaptive trophic niches of euryphagous fish species in response to off‐seasonal water level regulation in Hongze Lake. Animals, 15(1), 59. 10.3390/ani15010059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. MacArthur, R. H. , & Pianka, E. R. (1966). On optimal use of a patchy environment. American Naturalist, 100(916), 603–609. 10.1086/282454 [DOI] [Google Scholar]
  41. Medeiros, E. S. , & Arthington, A. H. (2014). Fish diet composition in floodplain lagoons of an Australian dryland river in relation to an extended dry period following flooding. Environmental Biology of Fishes, 97, 797–812. 10.1007/s10641-013-0180-0 [DOI] [Google Scholar]
  42. Mérona, B. D. , & Rankin‐de‐Mérona, J. (2004). Food resource partitioning in a fish community of the central Amazon floodplain. Neotropical Ichthyology, 2, 75–84. 10.1590/S1679-62252004000200004 [DOI] [Google Scholar]
  43. Mugnai, R. (2010). Manual de identificação de macroinvertebrados aquáticos do Estado do Rio de Janeiro: Para atividades técnicas, de ensino e treinamento em programas de avaliação da qualidade ecológica dos ecossistemas lóticos. Technical Books. [Google Scholar]
  44. Nanjappa, P. , Brand, L. , & Lannoo, M. J. (2000). Swimming patterns associated with foraging in phylogenetically and ecologically diverse American weakly electric teleosts (Gymnotiformes). Environmental Biology of Fishes, 58(1), 97–104. 10.1023/A:1007656801949 [DOI] [Google Scholar]
  45. O'Gorman, E. , Kalinkat, G. , Brose, U. , Jochum, M. , Boy, T. , Gauzens, B. , Kortsch, S. , & Rosenbaum, B. (2021). Flexible foraging behaviour increases predator vulnerability to climate change. Nature Climate Change, 14, 387–392. 10.1038/s41558-024-01946-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Oksanen, J. , Simpson, G. , & Blanchet, F. G. (2022). vegan: Community Ecology Package (Version 4.2.1). https://cran.r-project.org/web/packages/vegan/
  47. Oliveira, A. , Dias, F. , Sousa, E. , Bussons, M. , De Freitas, P. , Garcês, L. , & Bussons, I. (2025). Feeding ecology of Osteoglossum bicirrhosum (Osteoglossiformes: Osteoglossidae) in a Central Amazon reserve. Anais da Academia Brasileira de Ciências, 97(2), e20241121. 10.1590/0001-3765202520241121 [DOI] [PubMed] [Google Scholar]
  48. Peel, M. C. , Finlayson, B. L. , & McMahon, T. A. (2007). Updated world map of the Köppen‐Geiger climate classification. Hydrology and Earth System Sciences, 11(5), 1633–1644. 10.5194/hess-11-1633-2007 [DOI] [Google Scholar]
  49. Penha, I. C. S. , Jardim, A. A., Jr. , Prata, E. G. , Seabra, L. B. , Gusmão, R. R. , Ferreira, G. T. , Costa, A. F. V. N. M. , Freitas, T. M. S. , & Montag, L. F. A. (2025). Feeding ecology of a detritivorous fish in controlled flood pulse area in the Amazon revealed by stomach content and stable isotopes analyses. Aquatic Sciences, 87(1), 1–13. 10.1007/s00027-024-01147-x [DOI] [Google Scholar]
  50. Penha, I. C. S. , Seabra, L. B. , Prata, E. G. , Freitas, T. M. S. , & Montag, L. F. A. (2024). Unravelling a specialised diet of an Amazonian catfish in a controlled flood‐pulse area by combining stomach‐content and stable‐isotope analyses. Marine and Freshwater Research, 75, 23039. 10.1071/MF23039 [DOI] [Google Scholar]
  51. Pereyra, P. E. R. , Hallwass, G. , Poesch, M. , & Silvano, R. A. M. (2021). ‘Taking fishers’ knowledge to the ‘lab’: An interdisciplinary approach to understand fish trophic relationships in the Brazilian Amazon. Frontiers in Ecology and Evolution, 9, 723026. 10.3389/fevo.2021.723026 [DOI] [Google Scholar]
  52. Petry, A. C. , Agostinho, A. A. , & Gomes, L. C. (2003). Fish assemblages of tropical floodplain lagoons: Exploring the role of connectivity in a dry year. Neotropical Ichthyology, 1, 111–119. 10.1590/S1679-62252003000200005 [DOI] [Google Scholar]
  53. Petsch, D. K. , Cionek, V. D. M. , Thomaz, S. M. , & Dos Santos, N. C. L. (2023). Ecosystem services provided by river‐floodplain ecosystems. Hydrobiologia, 850(12), 2563–2584. 10.1007/s10750-022-04916-7 [DOI] [Google Scholar]
  54. Prata, E. G. , Seabra, L. B. , Neres‐Lima, V. , Montag, L. F. A. , & Freitas, T. M. S. (2025). Diet composition and isotopic analysis unveil trophic dynamics of a fish in a controlled flood pulse area of the Amazonia. Hydrobiologia, 852(3), 689–703. 10.1007/s10750-024-05716-x [DOI] [Google Scholar]
  55. Pyke, G. H. , & Starr, C. K. (2021). Optimal foraging theory. In Encyclopedia of social insects (pp. 677–685). Springer International Publishing. 10.1007/978-3-030-28102-1_90 [DOI] [Google Scholar]
  56. R Core Team . (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/ [Google Scholar]
  57. Rayner, T. S. , Pusey, B. J. , & Pearson, R. G. (2009). Spatio‐temporal dynamics of fish feeding in the lower Mulgrave River, north‐eastern Queensland: The influence of seasonal flooding, instream productivity and invertebrate abundance. Marine and Freshwater Research, 60(2), 97–111. 10.1071/MF08055 [DOI] [Google Scholar]
  58. Rodrigues, P. P. , Machado, M. D. A. , Pety, A. M. , Silva, D. D. S. , de Souza, A. C. P. , Pieczarka, J. C. , & Nagamachi, C. Y. (2021). Archolaemus janeae (Gymnotiformes, Teleostei): First insights into karyotype and repetitive DNA distribution in two populations of the Amazon. Ecology and Evolution, 11(22), 15468–15476. 10.1002/ece3.8092 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Röpke, C. P. , Ferreira, E. , & Zuanon, J. (2014). Seasonal changes in the use of feeding resources by fish in stands of aquatic macrophytes in an amazonian floodplain, Brazil. Environmental Biology of Fishes, 97, 401–414. 10.1007/s10641-013-0160-4 [DOI] [Google Scholar]
  60. Ru, H. , Zhong, L. , Wei, N. , Li, Y. , Sheng, Q. , & Ni, Z. (2022). Variations of trophic structure and niche space in fish community along a highly regulated subtropical large river. Ecology and Evolution, 12(10), e9424. 10.1002/ece3.9424 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Sabaj, M. H. , Fitzgerald, D. B. , & Winemiller, K. O. (2024). Physicochemical and ecosystem properties of the middle to lower Xingu River, Brazil, prior to the operation of the Belo Monte dam complex. Proceedings of the Academy of Natural Sciences of Philadelphia, 166(1), 1–30. 10.1635/053.166.0120 [DOI] [Google Scholar]
  62. Seabra, L. B. , Huckembeck, S. , Freitas, T. M. S. , Lobato, C. M. C. , Penha, I. C. S. , Prata, E. G. , & Montag, L. F. A. (2025). Variation in basal sources contribution to the diet of a predator fish in an altered flood pulse area in the Amazon. Hydrobiologia, 852(4), 909–925. 10.1007/s10750-024-05736-7 [DOI] [Google Scholar]
  63. Silva, P. B. , Arantes, C. C. , Freitas, C. E. , Petrere, M., Jr. , & Ribeiro, F. R. (2021). Seasonal hydrology and fish assemblage structure in the floodplain of the lower Amazon River. Ecology of Freshwater Fish, 30(2), 162–173. 10.1111/eff.12572 [DOI] [Google Scholar]
  64. Stickler, C. M. , Coe, M. T. , Costa, M. H. , Nepstad, D. C. , McGrath, D. G. , Dias, L. C. , & Soares‐Filho, B. S. (2013). Dependence of hydropower energy generation on forests in the Amazon Basin at local and regional scales. Proceedings of the National Academy of Sciences of the United States of America, 110(23), 9601–9606. 10.1073/pnas.1215331110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Timpe, K. , & Kaplan, D. (2017). The changing hydrology of a dammed Amazon. Science Advances, 3(11), e1700611. 10.1126/sciadv.1700611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Vari, R. P. , de Santana, C. D. , & Wosiacki, W. B. (2012). South American electric knifefishes of the genus Archolaemus (Ostariophysi, Gymnotiformes): Undetected diversity in a clade of rheophiles. Zoological Journal of the Linnean Society, 165(3), 670–699. 10.1111/j.1096-3642.2012.00827.x [DOI] [Google Scholar]
  67. Wang, J. , Li, L. , Xu, J. , & Gu, B. (2016). Initial response of fish trophic niche to hydrological alteration in the upstream of three gorges dam. Ecological Processes, 5, 1–9. 10.1186/s13717-016-0055-3 [DOI] [Google Scholar]
  68. Winemiller, K. O. (1996). Factors driving temporal and spatial variation in aquatic floodplain food webs. In Food webs: Integration of patterns & dynamics (pp. 298–312). Springer US. 10.1007/978-1-4615-7007-3_29 [DOI] [Google Scholar]
  69. Woodward, G. , & Hildrew, A. G. (2002). Food web structure in riverine landscapes. Freshwater Biology, 47(4), 777–798. 10.1046/j.1365-2427.2002.00908.x [DOI] [Google Scholar]
  70. Zar, J. H. (2010). Biostatistical analysis. Pearson. [Google Scholar]
  71. Zavala‐Camin, L. A. (1996). Introdução aos estudos sobre alimentação natural em peixes. Editora da Universidade Estadual de Maringá. [Google Scholar]
  72. Zuanon, J. , Deus, C. , Magurran, A. , & Duarte, C. (2019). Trophic ecology of benthic fish assemblages in a lowland river in the Brazilian Amazon. Aquatic Ecology, 53, 707–718. [Google Scholar]

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Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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