Introduction

Freshwater fish assemblages are structured by variables related to both water quality and riparian vegetation1,2,3,4,5,6. In this sense, warmer waters exhibit higher fish abundance and biomass while highly oxygenated waters may lead to greater species diversity7,8,9. Riparian vegetation is a transitional semiterrestrial system10 that provides energy in watercourses through the input of organic matter2. Leaves deposited on the watercourse bed contribute indirectly to fish food because they act as a substrate for numerous microorganisms11 and insects12,13. In addition, riparian trees and roots restrict channel widening, cause channel deepening and add coarse woody debris favoring fish concealment and channel complexity.

The influences of water and riparian vegetation on fish assemblages are not independent2,10,14,15; that is, riparian vegetation may directly or indirectly influence water variables16. For example, water temperature is directly influenced by riparian vegetation, which regulates the watercourse insolation level17,18 and influences primary production19. Conversely, channel depth and substrate heterogeneity are indirectly influenced by riparian vegetation because the riparian zone regulates the entry of sediment that can be deposited into the watercourse10,20,21.

Another factor that should not be neglected is the spatial factor (e.g., the river network), which includes geographical barriers that hamper or prevent species migration between locations. Abundance and richness are diversity metrics that are spatially structured22,23,24,25,26,27,28. Spatial factors are a consequence of the geological and local climatic influence on the streams in a river network29,30,31 and the position of the watercourse along a longitudinal gradient (upstream-downstream32) for the 1st-3rd 33 and 4th-7th order34 streams. A spatial model coupled with a river network accurately explains fish richness patterns35,36. Additionally, the river network acts as a corridor37, facilitating fish species dispersion38 or acting as a filter39.

Furthermore, the individual influence of water, riparian or spatial processes on the structure of fish assemblages is not necessarily consistent40. Instead, the influence of these processes results more often from their interaction41. Therefore, physical habitat variables influence fish assemblages either alone27 or in combination with water quality variables42.

The aim of this paper was to partition and quantify the influence of riparian, physical habitat, water quality and spatial variables on fish assemblages in headwater streams located in the Upper Paraná River basin, Central Brazil.

Results

A total of 4879 specimens belonging to 59 species and 19 families were collected (Table 1).

Table 1 Number of individuals (n) and fish species collected in the stream sites sampled in the Upper Paraná River basin, Central Brazil, between April and September 2009.
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Influence of the environmental conditions on fish assemblages

The variation partitioning analysis indicated that fish abundance variation is explained by water quality (18.7% of variation), physical habitat (8.4%), spatial (6.2%) and riparian zone variables (5.1%; Fig. 1). The interactions among the spatial variables, water quality and physical habitat explain 16.7% of the variation, those among the physical habitat, riparian zone and water quality explain 8.9%, those between the physical habitat and water quality explain 8.2%, and the other interactions represent ≤3.4% (Fig. 2). The Procrustes analyses indicated a significant correlation between the fish abundance and the physical habitat (M2 = 0.295; p < 0.001) and water quality variables (M2 = 0.565; p < 0.001) and no significance for the riparian zone (M2 = 0.200; p = 0.526) or spatial variables (M2 = 0.150; p = 0.744). All the non-metric multidimensional scaling NDMS analyses performed had a good fit (stress < 0.02).

Figure 1
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Variation partitioning (percentage) of stream fish richness among physical habitat, water quality, riparian zone and spatial compartments.

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Figure 2
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Analyses of redundancy (RDA) output correlating stream fish assemblage to environmental water variables. aspfus = Aspidoras fuscoguttatus; astalt = Astyanax altiparanae; astfas = Astyanax fasciatus; astsca = Astyanax scabripinnis; brystr = Bryconamericus stramineus; poeret = Poecilia reticulate; piaarg = Piabina argentea; steins = Steindachnerina insculpta; CO = conductivity; CL = chlorophyll concentration; MO = organic matter; CW = channel width; TU = turbidity; DO = dissolved oxygen; CD = channel depth; WT = water temperature. P1 – P22 = stream sites. Only species with >90.0% of contribution to the structure of RDA are represented.

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Fish assemblages-environmental conditions relationships

According to the broken stick criteria, there were two significant axes for the PCAs performed separately on the water quality (77% of the variation) and physical habitat (81% of the variation), eight axes for the PCA performed on the riparian zone (87% of the variation) and three significant MENs (Moran’s Index = 0.01 for each one) for the spatial variables.

The multiple linear regression showed no significant relation between fish abundance and the variables of the four groups considered (r2 = 0.566; F (12, 27) = 2.536; p = 0.128). In contrast, a significant relation was observed between fish richness (r² = 0.784; F (12, 27) = 5.865; p = 0.001) and the PCA-1 of the physical habitat variables (p = 0.005; Table 2). All of the other compartments did not display significant relationship (Table 2).

Table 2 Multiple regression statistics between the fish richness attribute and the variables of the physical habitat (PH), water quality (W), riparian zone (RZ) and spatial (SP) and compartments represented by principal component axes (PCA); see the methodological section for more details. The contribution of each variable is displayed. SC = Standard coefficient; VIF = Variable Inflation Factor; t = Student t test. *Significant probabilities (p < 0.05).
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The relationship between assemblage and physical habitat variables is detailed by the RDA (total variance explained by the two axes = 53.4%; F (10, 17) = 3.543; p = 0.003). The first axis (35.17%) was positively correlated with conductivity, dissolved oxygen and chlorophyll concentration and negatively correlated with water temperature, whereas the second axis (18.23%) was positively correlated with organic matter, channel depth, pH and channel width and negatively correlated with turbidity (Fig. 2). The characins Piabina argentea and Astyanax altiparanae and the scrapetooths Parodon nasus were related to high values of water conductivity, dissolved oxygen and chlorophyll concentration, whereas the characins Astyanax fasciatus and Astyanax scabripinnis and the poeciliid Poecilia reticulata were associated with elevated water temperature values. The scrapetooths Apareiodon ibitiensis, the headstander Leporinus microphthalmus, and the toothless Steindachnerina insculpta were associated with elevated organic matter and pH values and a large and deep channel stream. The characin Bryconamericus stramineus, the callichthyid armored catfish Aspidoras fuscoguttatus and the South American darter Characidium zebra were correlated with high values of turbidity (Fig. 2).

Discussion

The riparian zone does not display any significant influences on fish abundance or richness in the headwater streams sampled. Similar results using a different methodology were obtained for fish diversity43 in 1st to 3rd order headwaters streams in the Amazon region. This result suggests a low influence of riparian vegetation removal, assessed indirectly in this paper by the variables of the riparian zone group (type and percentage of the vegetation cover), on fish assemblages. However, studies focused on this subject have stressed the influence of the riparian zone on fish assemblages in the Amazon (channel fragmentation, deforestation44; mechanized agriculture43), São Francisco (deforestation42,45) and Paraná River basin (deforestation45), the last two of which contain the same vegetation cover of the area sampled in this paper (i.e., Cerrado).

The spatial component also showed no significant influence on fish assemblages. The abundance and richness of plants and animals, including stream organisms, are spatially structured45,46 because of the influence of geology, the local climate30 and the watercourse position along a longitudinal gradient32, especially for 1st to 3rd order streams33. However, if the 1st and 2nd order streams sampled in this study were in the same geologic (a combination of Precambrian metamorphic rocks, continental sedimentary rocks and tholeiitic basalts47) and climatic (tropical climate with a dry season) domain, a similarity of fish abundance and richness could be expected. It suggest that the influence of environmental conditions and resources appear to be more influent than the spatial process, even that the sample sites are located in different basins.

In this study, fish richness was influenced by physical habitat (stream channel width and depth, and organic matter) and water quality (conductivity, water temperature, pH, chlorophyll, dissolved oxygen, and turbidity) variables. These variables are known to structure not only fish assemblages4,48,49 but also their specific attributes, such as richness50,51,52,53. The results agree with those reported for Amazonian43 and Cerrado fish assemblages of 1st to 3rd order headwater streams42, although some previous studies did not separate the influence of physical habitat and water quality variables from those of the riparian zone, as was done in this paper. Additionally, these physical habitat and water quality variables are better predictors of fish assemblage variability than riparian or catchment variables43 or land use and the geophysical landscape42 in Amazon and Cerrado headwater streams, respectively.

The influence of water conductivity on fish assemblages, as observed in this study, was also reported for tropical54 and temperate watercourses51. Conductivity is a surrogate or correlate of water productivity, which influences freshwater fish body condition45, because it measures the electrical conductivity resulting from the concentration of dissociated ions55. Fish species can prefer aquatic habitats with specific requirements, such as elevated values of water conductivity, dissolved oxygen and chlorophyll concentration (as seen in the scrapetooths Parodon nasus and the characins Astyanax altiparanae and Piabina argentea in the watercourses sampled). In the case of P. nasus, the relationship observed is explained because this species is found in riffles56 where there are elevated levels of dissolved oxygen. Furthermore, P. nasus, a periphyton scraper that prefers rocky substrates where algae and bryophytes are abundant, is associated with waters with high conductivity because of eutrophication57. On the other hand, the characin A. altiparanae is considered tolerant to aquatic environmental changes and disturbances such as pollution58, which elevates water conductivity, and displays adaptations (i.e., a projection of the lower lip increase oxygen capture from water surface) to survive in low concentrations of dissolved oxygen56. Finally, the characin P. argentea is a midwater swimmer described as an opportunistic generalist species abundant in disturbed watercourses (modified from lotic to lentic conditions)59 that is also positively correlated to dissolved oxygen concentrations in streams of the Upper Paraná River basin60.

The poecilid P. reticulata, an exotic species in Brazilian watercourses, and the characin A. fasciatus are tolerant to habitat alterations57,61. Additionally, A. fasciatus and A. scabripinis (to a lesser extent62) are sensitive to water temperature because of the influence on their reproduction cycles63, whereas P. reticulata displays female-choice sexual selection64, fry production65, schooling behavior66, and aquatic surface respiration (ASR) to meet oxygen demand in hypoxic water67 regulated by the water temperature. These relationships explain the affinity of these species for the water temperatures found in the streams sampled. However, this affinity, especially for P. reticulata and A. scabripinis, can change during the low- and high-water seasons, when both species are associated with low water temperature68.

The accumulation of organic matter, such as trunks and bundles of leaves, may be responsible for species coexistence in different habitats. This coexistence can occur because of the increase in habitat heterogeneity resulting from organic matter input69,70 from the surrounding riparian zone or the transport of leaves and other matter from upstream to downstream71,72,73,74,75, which are then deposited in stream areas with low water velocity76. This seems to be the case in this study for the scrapetooths Apareiodon ibitiensis, a detritivorous species that scrape the algal film adhered on the surfaces of rocks and logs77, the toothless characin Steindachnerina insculpta, a bottom feeding fish55, and the headstander Leporinus microphthalmus, which, like other anostomids, feeds on sponges, detritus, insects, seeds, leaves, and filamentous algae, in the substrate78,79.

Additionally, the preference of these species for relatively large and deep streams can be related to their body length (A. ibitiensis = 11.3 cm, S. insculpta = 16.1 cm, L. microphthalmus = 11,8 cm54), as reported for A. ibitiensis80. However, the results found can be influenced by local or regional modifications. For example, the fragmentation of a channel or watercourse and local/regional deforestation influence the organic matter inputs (leaves, trunks and stems in this case), habitat complexity and riverbed stability. This, in turn, influences fish richness, as pointed out for Amazonian headwater streams44.

Among the species sampled, the callichthyid armored catfishes Aspidoras fuscoguttatus, the characin Bryconamericus stramineus and the South American darter Characidium zebra are associated with high water turbidity. The callichthyid A. fuscoguttatus is a bottom dwelling species that swims near the watercourse substrate gathering food (“grubber excavating while moving”81). This behavior can explain its ability to exploit the watercourse substrates, which are covered by fine sediments56 that are transported by water, and its capacity to survive in streams that have remarkable seasonal oscillation in turbidity, with lower values during the dry period and higher values in the rainy period82. On the other hand, the characin B. stramineus is a predominantly insectivorous83 active swimmer84 that is abundant in shallow streams of the Upper Paraná basin with elevated turbidity83,85 and water velocity85. The relationship of C. zebra with water turbidity is unexpected considering that it is an indicator species of pristine environments, with a sit-and-wait behavior for capturing prey86 and rheophilic preferences that can be affected by high levels of suspended sediments in the water column and the resulting siltation of the substrate54.

Among the four groups of environmental variables considered, only those related to the physical habitat and water quality significantly influenced the richness of the fish assemblages. This influence is explained by the interaction of the fish assemblages with nine variables (conductivity, water temperature, pH, chlorophyll, organic matter, dissolved oxygen, turbidity, channel width and channel depth). These results indicate that local instream characteristics of headwater streams have more influence on fish assemblages than factors associated with the riparian zone in Cerrado river basin draining areas. The comparison between these findings and those from the Amazon River basin suggests that this influence exists regardless of the river basin and its vegetation cover (Cerrado and Amazon in this case).

Materials and Methods

Study area

Twenty-seven sites (one sample site per stream) of the 1st and 2nd order tributaries of the Meia Ponte River (seven streams; 2.7 to 10.2 km apart from each other), Piracanjuba River (14; 4.8 to 17.8 km) and Santa Maria River (six; 4.8 to 6.0 km) were sampled, all of which are located in the Southeast Region, Goiás state, Upper Paraná River basin, Central Brazil (Fig. 3, Table 3). Sampling was conducted between April and September 2009, which corresponded with the dry season of the regional climate (Aw per the Köppen-Geiger classification). The Paraná River basin drainage is located on sedimentary deposits corresponding with the Paleozoic and Cenozoic and covered by basalt from the Jurassic-Cretaceous age47. The sampling stations are located on a combination of three types of rocks: i) Precambrian metamorphic rocks; (ii) continental sedimentary rocks; and (iii) tholeiitic basalts, which are abundant in the Paraná basin47. The vegetation cover of the Meia Ponte and Piracanjuba River basin was deciduous forest, and that of the Santa Maria basin was a semideciduous forest, all of which belong to the Cerrado (the Brazilian savanna biome).

Figure 3
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Location of streams sampled (black circles) from April to September 2009 in the Upper Paraná River basin, Central Brazil. The black area in the Paranaíba River represents the Itumbiara hydroelectric reservoir.

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Table 3 Geographic coordinates and local geomorphological characteristic of stream sites sampled between April and September 2009 in the Upper Paraná River basin, Central Brazil. MP = Meia Ponte, PI = Piracanjuba, SM = Santa Maria, SD = Standard deviation.
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In each stream, one 100-m site was selected according to its accessibility, marked and georeferenced (Garmin GPSMAP64. Each site was divided into 11 transects, one every ten meters, where the data collection for both fish assemblages and variables was performed.

All sites were away from urban areas and were found in a landscape matrix formed mainly by pasture. The exception was site P17, which was surrounded by a sugarcane crop. The sites sampled had riparian vegetation covering the stream channel and at least one opening, which was intended for watering livestock or replaced by grass for feeding cattle (site P5), in the riparian cover along the site. The channel depth of the stream sites ranged from a minimum of 0.10 (P2 and P20) to a maximum of 0.53 m (P12), whereas the channel width ranged from 0.60 (P7) to 7.78 m (P14; Table 3). The predominant substrate in the sites sampled was sand, except in P4, P13, P19 (gravel) and P11 (rocky outcrops; Table 3). The predominant aquatic habitat type was lotic except in stretch P9. Upstream site P17 was located in a reservoir.

Sampling protocols

Sixteen environmental variables were measured in each site. Six variables were associated with physical habitat, six with water quality and four with the riparian zone (Table 4).

Table 4 Environmental variables by compartment measured in the stream sites sampled in the Upper Paraná River basin, Central Brazil, between April and September 2009.
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Riverbank substrate, riverbank slope, aquatic habitat, type of riparian vegetation cover and percentage of riparian vegetation cover were visually characterized at each transect (along both riverbanks) along with luminosity (photometer; Polaris), stream channel width (measuring tape), stream channel depth (graduated rope) and water velocity (flowmeter; General Oceanic 2030). At the initial, middle and final transects of each site, organic matter samples of the stream channel bed and water were collected to determine algae biomass and to measure the physical and chemical variables.

Organic matter was collected using a Surber sampler (30 × 30 cm). In the laboratory, the samples were dried at 100 °C for 24 hours and weighed (SC2020 – Ohaus; 0.001 g)87.

Alpha chlorophyll concentration was used as a reliable and common proxy for the total phytoplankton biomass88, which may vary according to the degree of shading caused by riparian forests in headwater streams19. In the field, 25 L of water was filtered directly from the stream using a plankton net (mesh 1 μm) and a water pump (P835; Stihl). The product of the filtering process was placed in a 600 ml opaque bottle containing 1 ml of saturated magnesium carbonate. In the laboratory, the samples were filtered (cellulose ester membrane; porosity 0.45 μm) and quantified by spectrophotometry (spectrophotometer; Varian-Cary-50 CONC)89. The a, b and c chlorophyll concentrations were calculated following the Jeffrey and Humphrey equation90.

Water turbidity (turbidimeter; LaMotte 2020), temperature and conductivity (thermometer/conductivity meter WTW 3015i) and dissolved oxygen (DO-Lutron 5510) were measured at ~20 cm depth. The water turbidity, temperature, conductivity, dissolved oxygen and water velocity were measured at ~20 cm depth, whereas luminosity and air temperature were measured at ~20 cm above the water surface.

Fish were collected by shore electrofishing (electrofisher DC, 100–600 V plugged into a 220 V electric generator) modified from91; that is, the site’s length was 100 m and traversed only one time instead of being 50–80 m in length and traversed three times. Both modifications were performed based on the results of92, taking into account the logistics of the electrofishing gear used and displacement difficulties that occur along Cerrado streams because of physical conditions (e.g., trunks and steep stream bank). Four people collected samples for one hour in each site. The collected fish were placed in plastic bags, euthanized with a saturated clove oil solution and fixed in formalin (10%). All the bags were identified with tags containing the stream and site code. Fish was collected in the dry season when captures are more efficient because of lower water levels93. Fish sampling, transport and preservation of the sampled specimens were carried out in accordance with the relevant guidelines and regulations of the Sistema de Autorização e Informação em Biodiversidade, Instituto Chico Mendes de Conservação da Biodiversidade, Ministerio do Meio Ambiente (license # 20226 granted to the second author).

Data analysis

The dataset was organized into five matrices. The first matrix was composed of species abundance (the total number of individuals per species). The second consisted of physical habitat variables (frequency values by category or average values): stream channel width and depth, stream channel substrate, aquatic habitat, water velocity and organic matter. The third consisted of water quality variables (average values): turbidity, water temperature, conductivity, pH, dissolved oxygen and chlorophyll concentration. The fourth consisted of variables related to the riparian zone (frequency values by category): riverbank substrate, riverbank slope, type of riparian vegetation cover and percentage of riparian vegetation cover in the channel. The fifth data matrix grouped the main spatial eigenvectors (MENs)94, which constitute a representation of the spatial process resulting from the analyses performed on the spatial data matrix (geographic coordinates) considering a linear distance (Euclidean distance) between sampling points. The MENs represent spatial autocorrelations (Moran’s index) and can be used as a surrogate for the dispersion ability of species94,95. Significant MENs were considered those with Moran’s index values < 0.05. All the procedures to obtain the MENs were performed in SAM macroecology software96.

To determine the influence of the variable groups (physical habitat, water quality, riparian zone and spatial) (environmental variables) on the fish (biotic structure), a variation partitioning analysis was performed. After that, each data matrix was transformed to a similarity matrix using a specific index (Bray-Curtis for fish species abundance and Euclidean distance for all the other data matrices) and nonparametric multidimensional scaling (NMDS) was performed97. Using the resulting NMDS, a correlation (Procrustes analysis98) was performed separately between the fish assemblages and the physical habitat, water quality, riparian zone and spatial groups (9999 permutations99).

To determine the relationship between the fish assemblages and the variable groups (physical habitat, water quality and riparian zone), two multiple linear regressions were performed: the first one was for fish species richness, and the second one was for fish species abundance. A principal component analyses (PCA) was performed separately on each variable’s group (physical habitat, water quality, riparian zone). The significant axes were retained based on the broken stick criteria and used to perform the multiple linear regressions. The PCA axes were used in place of the original variables to avoid multicollinearity.

Finally, redundancy analyses (RDA), which consider the percentage of explained variation (R²) followed by a bootstrap procedure100, were performed to test the interaction between fish and the physical habitat, water quality and riparian zone groups. These analyses were performed only for the data matrices with significant relationships with the fish matrices (abundance and/or richness). All the statistical analyses were performed in R software using the vegan package98.