PRIMARY RESEARCH PAPER

Cumulative ecological effects of a Neotropical reservoircascade across multiple assemblages

Natalia Carneiro Lacerda dos Santos . Emili Garcıa-Berthou .

Juliana Deo Dias . Taise Miranda Lopes . Igor de Paiva Affonso .

William Severi . Luiz Carlos Gomes . Angelo Antonio Agostinho

Received: 24 July 2017 / Revised: 15 April 2018 / Accepted: 22 April 2018 / Published online: 2 May 2018

� Springer International Publishing AG, part of Springer Nature 2018

Abstract Dams have altered the physiography and

ecology of large rivers, causing severe environmental

changes at a global scale. Assuming that series of

reservoirs induce physical, chemical, and biological

longitudinal changes in rivers, we tested the hypothe-

ses that (i) the structure of biological communities in

reservoir cascades is not only affected by changes in

water quality, but also by cumulative hydrological

alteration and impacts on river connectivity; and (ii)

fish are more affected by cumulative effects of

reservoirs when compared to other aquatic assem-

blages. Samplings of three assemblages (phytoplank-

ton, benthic macroinvertebrates, and fish) were

conducted in the reservoir cascade of Sao Francisco

River, Brazil. We estimated the relative role of

environmental and spatial predictors through variation

partitioning analyses. Environmental variables, cumu-

lative reservoir volume, longitudinal position, and

distances from nearest reservoirs were used as

explanatory variables. Environmental variables were

the most important for the phytoplankton community.

No significant effects of the predictors used were

found for benthic macroinvertebrates, whereas spatial

variables and cumulative reservoir volume were the

most important predictors for fish. Therefore, our

results provide evidence of impacts along reservoir

Electronic supplementary material The online version ofthis article (https://doi.org/10.1007/s10750-018-3630-z) con-tains supplementary material, which is available to authorizedusers.

Handling editor: Andre Padial

N. C. L. dos Santos (&) � T. M. Lopes �L. C. Gomes � A. A. AgostinhoNucleo de Pesquisas em Limnologia, Ictiologia e

Aquicultura – Programa de Pos-graduacao em Ecologia de

Ambientes Aquaticos Continentais, Universidade

Estadual de Maringa, Av. Colombo, 5790, Maringa,

PR 87020-900, Brazil

e-mail: natalia.ictio@gmail.com

N. C. L. dos Santos � E. Garcıa-BerthouGRECO, Institute of Aquatic Ecology, University of

Girona, Campus de Montilivi, 17003 Girona, Spain

J. D. Dias

Departamento de Oceanografia e Limnologia,

Universidade Federal do Rio Grande do Norte, Via

Costeira Senador Dinarte Medeiros Mariz, Natal,

RN 59014-002, Brazil

I. P. Affonso

Universidade Tecnologica Federal do Parana - Campus

Ponta Grossa, Av. Monteiro Lobato S/N, Ponta Grossa,

PR 84016-210, Brazil

W. Severi

Departamento de Pesca e Aquicultura, Laboratorio de

Limnologia, Universidade Federal Rural de Pernambuco,

Av. Dom Manoel de Medeiros, 68, Dois Irmaos, Recife,

PE 52171-900, Brazil

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Hydrobiologia (2018) 819:77–91

https://doi.org/10.1007/s10750-018-3630-z

cascades, and suggest that their effects mainly influ-

ence fish assemblages.

Keywords Dams � Fish � Macroinvertebrates �Phytoplankton � Variation partitioning

Introduction

The role of the natural flow regime in the maintenance

of ecological integrity and biodiversity patterns is well

understood (Poff et al., 1997; Bunn & Arthington,

2002; Poff & Zimmerman, 2010). Complex interac-

tions between flow regime and physical characteristics

of habitats represent one of the greatest determinants

of the distribution, abundance, and diversity patterns

for riverine organisms (Townsend & Hildrew, 1994;

Poff et al., 1997; Ward et al., 1999; Bunn &

Arthington, 2002). From an ecological perspective,

aquatic species have evolved strategies and structures

in response to particular hydrological regimes (Bunn

& Arthington, 2002). For example, extreme events

such as floods exert selective pressures over aquatic

populations and determine the relative success of

different species (Junk et al., 1989; Poff et al., 1997;

Bunn & Arthington, 2002).

Anthropogenic disturbances of flow regime at

various scales have been pointed out as causes of

changes in freshwater diversity patterns (Poff &

Zimmerman, 2010; Simoes et al., 2015). Among the

main causes of disturbance, reservoirs stand out as

drivers of profound alterations in the physiography of

large rivers (Rosenberg et al., 2000) and are consid-

ered as the greatest global threats to the diversity and

integrity of freshwater ecosystems (Votosmarty et al.,

2010; Winemiller et al., 2016). The increases in the

number of dams all over the world, in general for

power generation purposes, are often configured in a

row in a single river or basin, thus forming reservoir

cascades. The alterations in hydrological dynamics of

fluvial systems and other impacts may be a result of the

synergistic effects of accumulative reservoirs.

Among the impacts of a dam construction, habitat

fragmentation and flow regulation (usually considered

separately) are the two most severe ones (Nilsson

et al., 2005; Grill et al., 2015). Fragmentation implies

the loss of connectivity among habitats, and is

especially detrimental to migration and dispersal of

organisms (Agostinho et al., 2007; Ziv et al., 2012),

with consequent implications for the structure of

communities and biodiversity patterns (Poff et al.,

1997). On the other hand, dams cause strong changes

in natural flow regimes (Grill et al., 2015). The

redistribution of runoff results in decreasing season-

ality and flow variability (Poff et al., 1997), inhibiting

flow peaks and increasing the frequency of short

pulses (Magilligan & Nislow, 2005; Agostinho et al.,

2007). Negative consequences of such alterations for

many aquatic species include deprivation of access to

different habitats necessary for reproduction whose

availability is regulated by the hydrological regime

(Bunn & Arthington, 2002; Agostinho et al., 2004).

Studies on freshwater ecosystems affected by dams

often regard to impacts from reservoirs in isolation,

whereas the effects of multiple dams in a hydrographic

basin are less investigated (Castello &Macedo, 2015).

In general, the construction of dams often causes

alteration in the transport of suspended particles and

dissolved substances, significant retention of sedi-

ments and nutrients, increase in overall temperature

and changes in the thermal regime downstream,

decrease in turbidity and pH, and indirect effects in

chemical and biological processes (reduction of

trophic chain length and primary production) (Stra-

skraba, 1990; Thornton, 1990; Barbosa et al., 1999).

These processes are likely exacerbated along reservoir

cascades (Miranda & Dembkowski, 2016).

Similarly, studies evaluating the consequences of

hydrological alterations have focused on specific

components of biodiversity, paying little attention to

the whole structure of the ecosystem. Some tendencies

have been evidenced for particular assemblages. For

example, for the phytoplankton community, Silva

et al. (2005) suggested that hydrology is the factor that

most affects assemblage structures in cascading

reservoirs, while Nogueira et al. (2010) reported a

negative effect of dams in species richness, associating

greater richness to non-regulated stretches. For ben-

thic macroinvertebrates, the importance of environ-

mental heterogeneity in determining composition and

distribution of assemblages along the reservoir cas-

cade has been pointed out, emphasizing a relationship

with the position of the reservoir in the basin (Behrend

et al., 2012; Santos et al., 2016). Finally, changes in the

composition of fish assemblages are predicted within

(Oliveira et al., 2004; Ferrareze et al., 2014; Miranda

& Dembkowski, 2016) and between reservoirs (Chick

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78 Hydrobiologia (2018) 819:77–91

et al., 2006; Miranda et al., 2008; Ferrareze et al.,

2014; Miranda & Dembkowski, 2016), in response to

hydrological and limnological dam-induced

alterations.

The goal of this study was to investigate the relative

role of natural upstream–downstream gradients, the

proximity of reservoirs (local effects of reservoirs such

as connectivity alterations), and the water storage

volume in physical–chemical variables and across

multiple assemblages (phytoplankton, benthic

macroinvertebrates, and fish). We hypothesized that

(i) the ecological communities in reservoir cascades

would not only be affected by physical–chemical

modifications of water quality, but also by factors such

as water storage volume and connectivity; and (ii) the

overall effects of reservoir cascades would vary

among ecological assemblages and would be stronger

at higher trophic levels such as fish assemblages. We

expected that alterations in the physical–chemical

variables would promote more pronounced changes in

the phytoplankton and benthic macroinvertebrates

assemblages, since variables such as temperature,

nutrients, and sediments are very important in regu-

lating these communities. On the other hand, we

expected that the effects of cumulative reservoir

volume along the river and dam-related connectivity

disruption would be more important for the fish

community, because hydrological alteration is well

known to affect fish habitat quantity and quality and

dams more strongly affect migration and dispersal in

fish than in other freshwater assemblages.

Materials and methods

Study area

This study was conducted in the Sao Francisco basin

(latitude between 7�000 and 21�000S, and longitude

between 35�000 and 47�400W), the third largest river

basin in Brazil with a drainage area of approximately

636,420 km2, occupying about 8% of the Brazilian

territory. Its medium and lower stretches are inserted

in the region known as Drought Polygon, in the

Brazilian Northeast, subjected to long periods of

drought and considered the most populated semiarid-

climate region in the world, with a rainy period from

January to April and mean annual precipitation of

350 mm (Silva & Molion, 2004).

In the last decades, the Sao Francisco River has

been subjected to successive damming, envisioning

power generation and navigation. The first great dam

was Tres Marias, built in 1961. Since the 1970s, six

other large dams (Sobradinho, Itaparica, Moxoto,

Paulo Afonso I–III, Paulo Afonso IV, and Xingo)

(Table 1) have been built in the middle and lower

stretches of the river, forming a sequence of reservoirs

(Godinho & Godinho, 2003). Currently, this basin has

its hydroelectrical potential highly exploited, with a

total inundated area of 5856.2 km2 (IBGE, 1999),

being considered the second largest in the country in

installed capacity of power generation.

The six studied reservoirs are located in the

medium (Sobradinho, Itaparica, Moxoto, Paulo

Afonso I, II, and III, (hereafter, PA I–III) and Paulo

Afonso IV (PA IV)) and lower reaches (Xingo) of the

Sao Francisco River. The first two of these reservoirs

are operated as an accumulation system and the others

as run-of-the-river systems (Fig. 1).

Sampling

Physical and chemical variables

Physical and chemical variables were sampled along

the reservoir cascade quarterly between October 2006

and July 2009 in Sobradinho reservoir, and between

December 2007 and September 2010 in others. A total

of sixteen physical–chemical variables and two vari-

ables of granulometric composition of the sediment

were measured (Table S1 in the Supplementary

Material). Temperature (�C), dissolved oxygen

(mg l-1), pH, total dissolved solids (TDS; mg l-1),

and electric conductivity (lS cm-1) were measured

with a multi-parametric probe. Water transparency

(m) was estimated with Secchi disk and turbidity was

measured with a turbidimeter (NTU).

Water samples collected from the surface for

determining the other variables were sampled with a

Van Dorn bottle of 2.5 l of capacity. Total phospho-

rous (lg l-1), inorganic phosphate (lg l-1), total

phosphate (lg l-1), and chloride (Cl) concentrations

were measured according to the methodology pro-

posed by APHA (2005). Total alkalinity (CaCO3) and

total hardness (CaCO3) were determined according to

Golterman et al. (1978). Total inorganic nitrogen—

TIN (lg l-1), Nitrate (N–NO3), ammoniacal nitrogen

(mg l-1 N), and Nitrite (N–NO2) were measured

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Hydrobiologia (2018) 819:77–91 79

according to Koroleff (1976) and Mackereth et al.

(1978). Finally, pigment concentrations (chlorophyll-

a and pheophytin) (lg l-1) were determined through

the method proposed by Nusch (1980) and recom-

mendations by Wetzel & Likens (2000). The classi-

fications of sediment texture and organic matter were

made according to Reichardt (1990) and EMBRAPA

(1999), respectively.

Biological communities

Phytoplankton

To test our hypotheses, three biological groups were

sampled in the six cascading reservoirs: phytoplank-

ton, benthic macroinvertebrates, and fish. Phytoplank-

ton sampling was performed quarterly in the same

period as the physical and chemical variables. For a

Table 1 Characteristics of the studied cascading reservoirs of the Sao Francisco river basin

Reservoir Altitude (m a.s.l.) Reservoir area (km2) Volume (hm3) Age (years) Type of operation

Sobradinho 388 4214 34.12 36 Accumulation

Itaparica 294 828 10.78 27 Accumulation

Moxoto 241 93 1.15 39 Run of the river

Paulo Afonso I-III 218 4.8 26.0 67 Run of the river

Paulo Afonso IV 239 12.9 127.5 36 Run of the river

Xingo 116 60 3.80 21 Run of the river

Fig. 1 Map of the study area with the location of the reservoir cascade in the Sao Francisco River

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80 Hydrobiologia (2018) 819:77–91

better representation of spatial variability and consid-

ering the diverse size of reservoirs, 30 sampling points

were selected in Sobradinho (19 inside the reservoir

and 11 downstream, in a lotic stretch before the

Itaparica reservoir), 12 in Itaparica, eight in Moxoto,

two in PA I–III, four in PA IV, and 11 in Xingo. Then,

the quantitative samples of phytoplankton were

obtained with amber glass bottles of 100 ml on the

surface of the water column and fixed in acetic Lugol

solution.

The phytoplankton community analysis was ini-

tially made with semi-permanent and permanent slides

for the identification of diatoms, made according to the

methodology proposed by Simonsen (1979) and

modified by Moreira Filho & Valente-Moreira

(1981). The identification and taxonomic placing of

the organisms were made with identification keys and

the following references: Prescott et al. (1982) and

Komarek & Fott (1983) for chlorophytes; Komarek &

Anagnostidis (1986, 2005) and Anagnostidis &

Komarek (1988, 1990) for cyanobacteria; Popovsky

& Pfister (1990) for dinoflagellates; Krammer &

Lange-Bertalot (1991) for diatoms; and John et al.

(2002) for other phytoflagellates. The quantitative

analysis was realized through the determination of the

organisms density (ind. l-1), according to the method

of Utermohl (Hasle, 1978).

Benthic macroinvertebrates

Macroinvertebrate sampling was conducted quarterly

between October 2006 and July 2009 in Sobradinho

and between December 2007 and September 2010 in

the other reservoirs, using a modified Peterson grab

sampler (0.0345 m2). Sampling stations were ran-

domly selected to represent the environmental vari-

ability within each reservoir (lotic, transition and

lentic zones). Accordingly, the number of sampling

stations varied among reservoirs, taking into account

the reservoir size and the seasonal variation in its

volume. At each sampling location, we took one

sample in the main body of the reservoir (limnetic

zone) and another near the shore (littoral zone).

Twelve sites were sampled in Sobradinho reservoir

(six in each of the limnetic and littoral zones). Eight

sites were sampled in Itaparica (four in each of the

limnetic and littoral zones), six in Moxoto (three in

each zone), two in Paulo Afonso I, II, and III (PA I–II–

III) (one in each zone), four in Paulo Afonso IV

(PAIV) (two in each zone), and eight in Xingo (four in

each zone).

At each site, three replicates were collected for

analysis of biological material, stored in plastic bags

and fixed in 4% formalin; an additional sample was

collected to analyze particle size and the content of

organic matter in the sediment. The particle size

composition of sediments (gravel in the sediment,

clay, silt, and sediment texture) was performed

according to the method of Reichardt (1990). The

phosphorus concentrations and organic matter content

were determined by the methods of EMBRAPA

(1999), and nitrogen analysis followed Mendonca &

Matos (2005). The environmental variables water

temperature (�C), pH, electric conductivity

(lS cm-1), and dissolved oxygen concentration

(mg l-1 O2) were determined in a vertical profile at

each site with a multi-parameter probe.

Laboratory analysis followed Santos et al. (2016).

The macroinvertebrates were identified and quantified

under stereomicroscope and optical microscopes at the

lowest possible taxonomic level (following Perez,

1988; Trivinho-Strixino & Srixino, 1995; Merritt &

Cummins, 1996; Dominguez & Fernandez, 2001;

Thorp & Covich, 2001) and preserved in 70% ethanol.

Fish

Fish sampling was performed bimonthly between

November 2006 and September 2009 in Sobradinho

reservoir and between February 2008 and December

2010 in the other reservoirs. The fluvial region

corresponding to a free stretch between Sobradinho

and Itaparica reservoirs was also sampled. The

sampling points were chosen aiming to represent the

variability of the reservoir, according to the extension

of the monitored area of the reservoir and the seasonal

variation of its volume. The largest reservoirs (So-

bradinho and Itaparica) were sampled in three differ-

ent zones (fluvial, transition, and lacustrine; sensu

Thornton, 1990), with one sampling point in each

zone. Smaller reservoirs (Moxoto, PA I–III, PA IV,

and Xingo) were sampled in areas close to the dam and

in the transition area.

Fish were captured with sets of gillnets (12, 15, 20,

25, 30, 35, 40, 50, 60, 70, 80, and 90 mm meshes,

50 m long each, and height of 1.44–4 m). Nets were

distributed in different regions of the reservoirs,

covering the different existing biotopes. Nets were

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Hydrobiologia (2018) 819:77–91 81

installed always at nightfall and collected in the

following morning, with an exposition time of circa

12 h. After sampling, fish were anesthetized using

benzocaine (up to 40 mg-l), euthanized and preserved

in 4% formalin solution. Each specimen was identified

to the species level according to Britski et al. (1984).

Data analysis

All physical and chemical variables, except pH, were

log-transformed (see Table S1 for specific transfor-

mations) to reduce their positive asymmetry and

provide the linearity of relationships assumed in the

ordination techniques. We first used Principal Com-

ponents Analysis (PCA) of the physical–chemical

variables and pigments concentrations to analyze their

main variation and confirm that it was caused by

spatial variation, mostly among reservoirs. We tested

for differences among reservoirs in means and

dispersals of the PCA scores with generalized least

squares (‘‘gls’’ function) in the statistical software R

(R Core Team, 2015).

We then used variation partitioning analysis to

estimate the relative role of environmental and spatial

predictors (cumulative reservoir volume, longitudinal

position, and distances from the nearest reservoirs

upstream and downstream) in the structure of aquatic

communities (Borcard et al., 1992; Legendre &

Legendre, 1998). For multiple response variables (as

our community data), variation partitioning (hereafter,

VP) uses a series of redundancy analyses for estimat-

ing howmuch of the variation in the response matrix is

explained uniquely and jointly by different sets of

predictors (Legendre & Legendre, 1998). Response

matrices consisted of physical and chemical or abun-

dance data (the latter transformed with Hellinger

distance) of different ecological assemblages (Legen-

dre & Gallagher, 2001). VP uses the adjusted R2 to

estimate the percentage of variation attributed to

unique and joint fractions (Beisner et al., 2006; Peres-

Neto et al., 2006). The use of the adjusted R2 is more

adequate because it does not depend on sample size

and the number of explanatory variables, and allows

the results to be comparable (Peres-Neto et al., 2006).

We used four sets of explanatory variables in VPs:

environmental variables, cumulative reservoir volume

(water stored in reservoirs upstream of a certain site),

longitudinal variation (altitude and distance to the

river mouth), and a matrix of the distances from

reservoirs. The environmental set consisted of the

physical–chemical variables (depending on their

availability for the different assemblages), with the

addition of granulometric variables for the analysis of

benthic macroinvertebrates. The variance inflation

factors (VIF) was computed to explore the multi-

collinearity. The VIF measure the proportion by which

the variance of a regression coefficient is inflated in the

presence of other explanatory variables (Borcard et al.,

2011). Environmental variables with a VIF higher than

five were removed from analyses (Zuur et al., 2009).

We did not perform the selection of variables for the

variation partitioning as recommended by Borcard

et al. (2011) specified on page 185.

The cumulative reservoir volume was used as an

indicator (proxy) for verifying the effect of the

regulated volume of water along the cascade. This

variable was the accumulated dammed volume

upstream of each sampling site along the basin, and

was the main indicator variable for the cumulative

effect of reservoirs. The dammed water volume for

each reservoir was obtained from the Hydroelectric

Company of Sao Francisco website (http://www.

chesf.gov.br/).

The longitudinal variation matrix is an indicator of

the spatial variation along the cascade, and contains

the distances of each sampling point to the mouth of

the river and the altitude. Finally, the matrix of

distance from reservoirs contains data of distance

between the sampling sites in the lotic stretch between

reservoirs (only between Sobradinho and Itaparica

remains a lotic stretch in the cascade) and the closest

upstream and downstream reservoir. This matrix of

distance from reservoirs was used as a proxy to

evaluate the connectivity of the communities in places

with free flow. The distances and altitude of each

sampling site were calculated using Google Earth

(http://earth.google.com/). For the benthic macroin-

vertebrates community, we used the environmental,

accumulated volume, and longitudinal matrices, since

this taxonomic group was not sampled in the lotic

region between reservoirs.

Additionally, a Mantel test (Mantel, 1967) was

performed to verify the correlation between the

predictor matrices (1000 permutations, significance

level P [ 0.05). There was significant correlation

between predictor variables. This was expected once

upstream reservoirs are situated in greater altitudes an

are classified as accumulation reservoirs, which

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82 Hydrobiologia (2018) 819:77–91

present higher residence time (see Table S2 Supple-

mentary Material).

We also performed a variation partitioning using

the environmental matrix as response variable and the

other matrices as predictor variables (accumulated

volume, longitudinal variation, and distance from

reservoirs) in order to verify the relative importance of

the predictors in the limnological variation along the

cascade.

Most analyses in this study were performed using

package ‘‘vegan’’ (Oksanen et al., 2017) in the

statistical software R. VP was obtained with the

function ‘‘varpart’’ and the significance of the pure and

overall VP components was evaluated through Monte

Carlo tests with 999 randomizations (Borcard et al.,

1992). Additionally, we performed a simple RDAwith

the aim of specifically evaluating the importance of

spatial predictors used as proxy for connectivity

(distance from reservoir) and cumulative volume that

expresses the cumulative effect of impacts on fish

community. This analysis was performed specifically

for this community as it is most affected by the

fragmentation of aquatic habitats and consequently

has negative effects on the dispersion processes.

Results

Physical and chemical variables

The two first axes of PCA (Fig. 2) summarized 46.5%

of the total variability and evidenced a clear separation

among reservoirs, particularly Sobradinho Reservoir,

which both PCA axes varied in means and variances

among reservoirs (generalized least squares, all P\0.005). The first axis mostly distinguished the most

upstream reservoir (Sobradinho), which displayed

more variable physical and chemical features due to

its larger size but had higher turbidity, phosphorous,

total phosphate, and total inorganic nitrogen concen-

trations and less pH and oxygen concentration. The

second axis mostly distinguished Xingo and Paulo

Afonso reservoirs, which had higher values of salinity,

conductivity, pigment concentrations, and inorganic

phosphate.

The variation partitioning for the environmental

matrix and the three groups of predictor variables

showed that all the pure effects were significantly

related to the physical–chemical variables along the

reservoir cascade (Fig. 3). The highest adjusted R2

value was for longitudinal variation (0.07). However,

the shared fraction between the variables accumulated

volume and longitudinal variation (0.15) revealed a

greater importance of the variation of the physical–

Fig. 2 Principal components analysis of the physical–chemical

variables in six cascading reservoirs of the Sao Francisco River.

Reservoirs: black, Sobradinho; red, Itaparica; green, Moxoto;

blue, Paulo Afonso I–III; grey, Paulo Afonso IV; orange, Xingo.

The ellipses correspond to the SD ellipses by reservoir. See

Methods for the transformations applied to the variables

Fig. 3 Variation partitioning (adjusted R2) for the physical and

chemical variables of the reservoir cascade in the Sao Francisco

River. The values shown are adjusted R2 (valuesB 0 not shown).

Bold values indicate significant effects (P \ 0.05); joint

variation is not testable

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Hydrobiologia (2018) 819:77–91 83

chemical parameters, although this fraction is not

testable.

Biological communities

The VP results for the community data differed

according to the assemblage analyzed. For phyto-

plankton, the unique effects of environmental vari-

ables (adjusted R2 = 0.07; P = 0.004) and cumulative

reservoir volume (adjusted R2 = 0.01; P = 0.002) were

significant but longitudinal variation and distance

from reservoirs were not (Fig. 4). No pure or overall

effects were significant (P [ 0.05) for the benthic

macroinvertebrate data, but the environmental vari-

ables explained the important variation fraction (ad-

justed R2 = 0.09; P = 0.14) (Fig. 5). For fish, the

unique effects of the four predictor sets were signif-

icant. Longitudinal variation (adjusted R2 = 0.05; P B

0.001) and distance from reservoirs (adjusted R2 =

0.03; P B 0.001) were the most important sources of

variation, each explaining around 11% of the overall

variation. Environmental variables (adjusted R2 =

0.02; P B 0.02) and the cumulative reservoir volume

(adjusted R2 = 0.02; P = 0.01) explained, respectively,

8 and 7% of the overall variation. However, pure

effects were in general low compared to joint effects

(Fig. 6). In a similar way, we observed important

shared fractions among other predictor matrices, as

longitudinal variation and distance from reservoirs

(adjusted R2 = 0.06), and Accumulated Volume and

distance from reservoirs (adjusted R2 = 0.05). These

results indicate that, in a general way, the effects

shared are more important.

To understand some of the most important VP

components, we explored in more detail the redun-

dancy analyses (RDA) for fish data. The first axis of

RDA of the fish data with cumulative reservoir volume

as a constraint explained 7% of the variation in the fish

community abundance (Fig. 7). This axis summarized

Fig. 4 Variation partitioning (adjusted R2) of phytoplankton

community data among four groups of explanatory variables:

environmental variables, accumulated volume, longitudinal

variation, and distance from reservoirs. Values correspond to

adjusted R2. Bold values indicate significant unique effects (PB

0.05); joint variation is not testable

Fig. 5 Variation partitioning (adjusted R2) of benthic macroin-

vertebrate community data among three groups of explanatory

variables: environmental variables, accumulated volume, and

longitudinal variation. Values correspond to adjusted R2. Bold

values indicate significant effects (P B 0.05); joint variation is

not testable

Fig. 6 Variation partitioning (adjusted R2) of fish community

data among four groups of explanatory variables: environmental

variables, accumulated volume, longitudinal variation, and

distance from reservoirs. Values correspond to adjusted R2.

Bold values indicate significant effects (P B 0.05); joint

variation is not testable

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84 Hydrobiologia (2018) 819:77–91

the relationship between hydrologic alteration and fish

composition: species such as Plagioscion squamosis-

simus (Heckel, 1840), Moenkhausia costae (Stein-

dachner, 1907), Triportheus guentheri (Garman,

1890), Curimatella lepidura (Eigenmann & Eigen-

mann, 1889), Tetragonopterus chalceus (Spix &

Agassiz; 1829), Leporinus reinhardtii (Lutken,

1874), Serrasalmus brandtii Lutken, 1875, Metynnis

maculatus (Kner, 1858), and Eigenmannia virescens

(Valenciennes, 1842) were more abundant in reser-

voirs with smaller accumulated volumes upstream. On

the other hand, Bryconops affinis (Gunther, 1864) and

Acestrorhynchus britskiiMenezes, 1969 were directly

related to the higher values of accumulated volume.

For the explanatory matrix of distance from reser-

voirs, the analysis revealed a relation of species as

Thriportheus guentheri, Curimatella lepidura, and

Plagioscion squamosissimus with reservoirs located

upstream the reservoir cascade, while species as

Acestrorhynchus britskii, Bryconopis affinis, and

Moenkhausia costae were associated with reservoirs

downstream (Fig. 8).

Discussion

The expectation that not only the changes imposed by

environmental variables were responsible for alter-

ations in the communities in the reservoir cascade was

partially supported by the results. This premise was

accepted for phytoplankton, which reported the

importance of the accumulated volume; and for fish,

where the variation in community structure was

explained also by the accumulated volume, longitudi-

nal variation, and distance of reservoirs. However, for

the benthic macroinvertebrate community no signif-

icant effects of the partitioning were evidenced for any

of the predictor variables. These results agree with

other studies that evaluated the role of dispersion and

local environmental variables in the structure of

aquatic communities (Beisner et al., 2006; Fernandes

et al., 2013; Padial et al., 2014; Petsch et al., 2015),

highlighting the importance of the spatial variables as

well as the environmental variables. However, despite

the robustness of VP, it is possible that the correlation

effect between some predictor matrices (such as

cumulative volume and longitudinal variation) has

some influence on the results, since it was not possible

to completely separate the effects. This may possibly

be associated with the fact that upstream reservoirs

with large accumulated volumes of water, and conse-

quently with great limnological difference, are located

in regions of higher altitudes.

With regard to environmental variables, we

observed marked longitudinal changes along the

reservoir cascade, evidenced by the clear separation

shown by the PCA. Sobradinho reservoir, the first in

the sequence, was separated from the other reservoirs

due to higher values of turbidity, total phosphorous,

inorganic phosphate, and total phosphate and lower

values of pH and dissolved oxygen. Reservoirs

Fig. 7 Redundancy analysis (RDA) of the fish community of

the Sao Francisco River basin, using cumulative reservoir

volume as a constraint. Cumulative reservoir volume increases

on the left of the diagram. Black points correspond to the site

scores

Fig. 8 Redundancy analysis (RDA) of fish community data

with distances from nearest reservoirs as constraints in the

reservoir cascade of the Sao Francisco River basin. Black dots

correspond to the scores of the sites

123

Hydrobiologia (2018) 819:77–91 85

designed for accumulation, such as Sobradinho,

usually have moderate to large capacity of accumu-

lation, are located in the medium stretch of rivers, and

have large inundated areas (Kennedy, 1999; Nogueira

et al., 2005). These characteristics make these reser-

voirs responsible for high rates of sedimentation of

coarse and fine organic particulate matter (CPMO/

FPMO) and nutrient retention, and higher turbidity.

The other reservoirs showed opposite patterns of

chemical variables, with an oligotrophication process

along the cascade, as described by Straskraba (1990)

and Barbosa et al. (1999), resultant from the decrease

in nutrients and turbidity, caused by solids retention in

upstream reservoirs.

Variation partitioning suggested that although

physical–chemical variables were independently

affected by the three predictor sets, the highest

variation percentage (15%) was explained jointly by

the longitudinal position and cumulative reservoir

volume. Therefore, although there is a strong colin-

earity between the degree of hydrologic alteration and

longitudinal position, both explain uniquely a small

part of the variation in physical and chemical prop-

erties. It is important to highlight that shared fractions

for limnological variables may be associated to some

degree with the correlation found between the matri-

ces of environmental variables, longitudinal variation,

and accumulated volume. Although rivers display a

continuous gradient in environmental conditions, and

ecological structure and functioning (Vannote et al.,

1980; Thorp & Delong, 1994; Thorp et al., 2006;

Humphries et al., 2014), dams and particularly series

of dams cause a rupture in this gradient (Ward &

Stanford, 1983, 1995). Furthermore, the direction and

extension of this displacement depend on various

factors, such as specific characteristics related to size,

depth, water intake position, retention time, and

position of the reservoir in the basin (Straskraba

et al., 1993). Therefore, our results suggest that

reservoir cascades accumulate changes in physical–

chemical variables along the river.

Variation partitioning of the biological communi-

ties supported our hypothesis that the effects of the

different predictor sets vary with the analyzed assem-

blage. In contrast to physical and chemical variables,

joint effects are less important, and the unique effects

are in general relatively more important for shaping

ecological communities, particularly for phytoplank-

ton, where physical and chemical water features were

the most influential predictor set. Although the

dispersal capacity and body size may also drive the

structure of metacommunities, assemblages with high

dispersal capacity and small body sizes, such as

phytoplankton, are generally more influenced by

environmental variables (Beisner et al., 2006; De Bie

et al., 2012; Padial et al., 2014, Urrea-Clos et al.,

2014). This fact explains the non-significance of

spatial predictive variables for this community, that

results from the high dispersion rates favored by

connectivity and unidirectional flow (massa effect

process) between reservoirs (Bortolini et al., 2017),

favoring mass transport of phytoplankton down-

stream. Phytoplankton is mostly regulated by a

combination of thermal regime and resource avail-

ability, mainly nutrient concentration and light avail-

ability (Temponeras et al., 2000; Lv et al., 2014). The

importance of nutrient concentration on the dynamics

of the phytoplankton community has been highlighted

in recent studies (Salmaso, 2010; Dong et al., 2012)

and the retention of nutrients and change of trophic

status along the cascade may be a key factor structur-

ing this community. Factors such as hydrology can

also drive the dynamics and structure of phytoplank-

ton by affecting light and nutrient availability

(Reynolds, 1993; Wu et al., 2013). In our case,

however, cumulative reservoir volume and physical–

chemical predictors acted jointly in shaping phyto-

plankton assemblages. In different Brazilian reservoir

cascades, no oligotrophication pattern was observed

and phytoplankton assemblages were more modulated

by hydrodynamics (Silva et al., 2005) or nutrient

inputs in specific reservoir factors (Nogueira et al.,

2010). In our study system, reservoirs located

upstream operate by accumulation, while the others

operate as run of the river. The influence of physical

parameters, such as light availability, on the phyto-

plankton community is expected to be more important

in upstream reservoirs, where nutrients are not limit-

ing; by contrast, nutrients would be more important

downstream. However, the presence of tributaries and

human disturbances surrounding reservoirs may drive

local patterns.

For the benthic macroinvertebrates, no predictor set

was significant although the percentages of explained

variance were similar than for other assemblages.

Many studies report the importance of the environ-

mental variables in structuring the macroinvertebrate

community (Peeters et al., 2004; Santos et al., 2016),

123

86 Hydrobiologia (2018) 819:77–91

that often reflect local conditions (Callisto et al.,

2005). The non-significant results might be due to

higher variability and spatial heterogeneity of this

community or more dependence on other unmeasured

factors, such as hydrological variability or morphom-

etry (e.g., hydrological connectivity or flow) (Heino,

2000; Gallardo et al., 2008; Obolewski, 2011; Holt

et al., 2015), although we included sediment features

that are known to markedly affect macroinvertebrate

assemblages (Santos et al., 2016).

For fish, the unexplained variation was lower than

in the other groups, the joint components higher, when

compared to phytoplankton and macroinvertebrates.

This agrees with previous studies that show that spatial

gradients are more important in structuring fish

assemblages (Beisner et al., 2006; Padial et al.,

2014). Longitudinal position jointly with cumulative

reservoir volume yielded most of the explained

variation. Miranda & Dembkowski (2016), in his

sawtooth wave concept, suggest that waves created by

successions of dams provoke changes in the longitu-

dinal patterns of the ichthyofauna due to the lacustrine

conditions created by dams along the river.

The proximity of reservoirs (the distances from

nearest reservoirs upstream and downstream) had a

significant unique effect on fish species composition

and overall is much more important than for environ-

mental data or phytoplankton. These results stress the

importance of connectivity for fish. The dam-free

stretch is approximately 300 km between the Sobrad-

inho and Itaparica reservoirs, and represents a relevant

area for the maintenance of the fish diversity. The area

provides large refuge and nursery areas among

degraded environments, being essential for the main-

tenance of the life cycle of many species, above all the

migrators (Agostinho et al., 2004; Miranda & Dem-

bkowski, 2016).

Cumulative reservoir volume was also important in

explaining fish species composition. This indicator of

hydrologic alteration jointly explained with the other

three predictors as part of the variation, suggesting that

increased cumulative reservoir volume, lower con-

nectivity, and as possible stronger physical–chemical

modifications along the cascade, results in a critical

situation for the ichthyofauna (Agostinho et al., 2007).

Another point that reinforces the negative results of

the joint effects is related to the redundancy analysis

with cumulative reservoir volume, which showed a

higher fish diversity upstream, where hydrological

alteration is less severe. In fact, a majority of

Neotropical freshwater fish are dependent on river

pulses for processes of gonadal maturation, migration,

spawning, and development of initial forms (Agos-

tinho & Julio Jr, 1999; Oliveira et al., 2015).

In summary, through the analysis of different

ecological assemblages, our results suggest that phys-

ical–chemical changes in a reservoir cascade are not

the only cause of negative effects, as other variables

such as cumulative reservoir volume and connectivity

also contribute to changes in the communities. In line

with several previous studies (e.g., Økland, 1999;

Møller & Jennions, 2002; Franco et al., 2018), we also

report low amount of explanation of all predictors.

More than using VP to make broad generalization in

community assembly, our study innovates by com-

paring biological groups and suggesting, even with

limitations detailed along the text, a likely role of

cumulative impacts along reservoir cascades, partic-

ularly for fish communities. Besides, we demonstrated

assemblage-specific responses to the different stres-

sors. Environmental variables were the most important

in explaining phytoplankton, while spatial variables

such as longitudinal position and distance from nearest

reservoirs had a more pronounced effect in the fish

assemblage. Although the pure fractions have been

significant and suggest negative effects on biological

communities attributed to reservoir cascades, the

shared effects are important given the correlations

found among some predictors, which prevents us from

making wider conclusions on isolated effects. Isolat-

ing the effect of predictor variables and understanding

the unique effect of these variables is an additional

step in the evaluation of the impact of cascade

reservoir systems on communities. Finally, the con-

struction and management of cascading reservoirs

should be carefully considered, given the possible

amplification of negative effects on the biota and

physical–chemical variables. Management and con-

servation plans should consider the maintenance and

proper ecosystem function of free stretches between

reservoirs or tributaries for the maintenance of the

longitudinal connectivity.

Acknowledgements This project was financed by the

Hydroelectric Company of the Sao Francisco – CHESF,

through the Foundation Apolonio Salles for Educational

Development – FADURPE. NCLS received a doctoral Grant

and Sandwich doctorate scholarship from the National Council

for Scientific and Technologic Development (CNPq), and the

123

Hydrobiologia (2018) 819:77–91 87

other authors received Grants from the Coordination for

Improvement of Higher Education Personnel (CAPES). EGB

was supported by the Spanish Ministry of Economy and

Competitiveness (Projects CGL2016-80820-R and CGL2015-

69311-REDT), the Government of Catalonia (ref. 2014 SGR

484), and CAPES (visiting professorship, ref. 88881.068352/

2014-01). JDD thanks CNPq to provide post-doctoral

scholarship. AAA has received productivity Grants from CNPq.

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Hydrobiologia (2018) 819:77–91 91

Supplementary Material

Cumulative ecological effects of a Neotropical reservoir cascade across multiple

assemblages

Effects of reservoir cascade on assemblages

Natália Carneiro Lacerda dos Santos1,2*, Emili García-Berthou2, Juliana Déo Dias3, Taise

Miranda Lopes1, Igor de Paiva Affonso4, William Severi5, Luiz Carlos Gomes1, Angelo

Antonio Agostinho1

1Núcleo de Pesquisas em Limnologia, Ictiologia e Aquicultura – Programa de Pós-

graduação em Ecologia de Ambientes Aquáticos Continentais, Universidade Estadual de

Maringá. Av. Colombo, 5790, 87020-900, Maringá, PR, Brazil

2GRECO, Institute of Aquatic Ecology, University of Girona, Campus de Montilivi,

17003 Girona, Spain

3Universidade Federal do Rio Grande do Norte, Departamento de Oceanografia e

Limnologia. Via Costeira Senador Dinarte Medeiros Mariz, 59014-002, Natal, RN, Brazil

4Universidade Tecnológica Federal do Paraná - Campus Ponta Grossa. Av. Monteiro

Lobato S/N, 84016-210, Ponta Grossa, PR, Brasil

5Universidade Federal Rural de Pernambuco, Departamento de Pesca e Aquicultura,

Laboratório de Limnologia. Av. Dom Manoel de Medeiros, 68, Dois Irmãos, 52171-900,

Recife, PE, Brasil

*Corresponding author: natalia.ictio@gmail.com

Table S1. Environmental variables measured along the reservoir cascade of the São Francisco River basin. Data transformation used for each

variable (when needed) is shown in the“transformation” column. a = minimum detected. Mean = mean value for each variable, Minimum =

minimum value for each variable, SD = standard deviation. The variable alkalinity was only used for the fish community

Environmental

variables Unit Transformation Physical-Chemical Macroinvertebrates Phytoplankton Fish

(n = 720) (n =394) (n =720) (n =305)

Mean Minimum SD Mean Minimum SD Mean Minimum SD Mean Minimum SD

PhysicalChemical

Variables

TIN µg L-1 log10 x + a 77.25 3.56 59.48 71.14 1.66 60.09 77.25 3.56 59.48 70.7 0.06 55.62

Pigments µg L-1 log10 x + a 5.98 0.56 6.1 6.65 0.68 8.84 5.98 0.56 6.1 5.26 0.561 2.67

Inorganic Phosphate µg L-1 log10 x 7.75 1.41 4.46 11.51 1.31 15.84 7.75 1.41 4.46 7.44 1.406 5.41

Total Phosphate µg L-1 log10 x 21.27 2.09 13.5 31.69 5.86 32.65 21.27 2.09 13.5 19.61 5.86 10.71

Total Phosphorous µg L-1 log10 x 62.23 4.96 49.95 48.88 9.91 28.06 62.23 4.96 49.95 59.82 7.43 36.79

Alkalinity

mg L-1

CaCO3 log10 x - - - 28.7 12.5 11.33 - - - 26.96 11.01 7.68

Total Hardness

mg L-1

CaCO3 log10 x 24.22 8.21 4.72 25.64 12.4 11.85 24.22 8.21 4.72 24.1 6.17 4.84

Chloride mg L-1 Cl log10 x + a 20.49 1.66 6.11 17.42 0.58 11.7 20.49 1.66 6.11 20.83 1.72 6.44

Turbidity UNT log10 x + a 9.48 0.8 11.88 10.42 0.7 9.64 9.48 0.8 11.88 8.72 0.11 11.22

Temperature °C log10 x 27.03 22.3 1.88 26.73 22.34 1.88 27.03 22.3 1.88 26.66 7.58 2.42

pH - - 7.97 6.97 0.41 7.78 2.55 0.99 7.97 6.97 0.41 8.01 6.73 0.43

Electric Conductivity µS cm-1 log10 x 63.82 36.00 19.84 85.57 45 70.65 63.82 0.05 26.96 - - -

Dissolved Oxygen mg L-1 log10 x + a 7.58 5.26 0.7 7.2 0.75 1.54 7.58 5.26 0.7 7.57 0.3 0.96

Salinity log10 x + a 0.03 0.02 0.01 0.04 0.02 0.05 0.03 0.02 0.01 0.04 0.02 0.01

TDS g L-1 log10 x 0.05 0.03 0.01 - - - 0.05 0.03 0.01 0.08 0.03 0.29

Secchi m log10 x + a 2.27 0.1 1.67 - - - 2.27 0.1 1.67 2.23 0.1 1.84

Granulometric

Variables

Clay % - - - - 37.25 0.7 23.98 - - - - - -

Organic Matter % - - - - 2.61 0.02 2.82 - - - - - -

Table S2. Results of the Mantel Test between all sets of predictor variables that are not part of

the variation of biological communities in the reservoir cascade of the São Francisco River.

Environmental

variables

Distance from

reservoirs

Longitudinal

variation

Cumulative

volume

Environmental variables 1

Distance from reservoirs 0.04 1

Longitudinal variation 0.16 -0.09 1

Cumulative volume 0.11 -0.14 0.84 1