cumulative ecological effects of a neotropical reservoir
TRANSCRIPT
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: [email protected]
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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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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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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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: [email protected]
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