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All content in this area was uploaded by Victor Emmanuel Lopes da Silva on Apr 06, 2018
Content may be subject to copyright.
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Content may be subject to copyright.
SPATIAL DISTRIBUTION OF JUVENILE FISH SPECIES IN NURSERY GROUNDS
OF A TROPICAL COASTAL AREA OF THE SOUTH-WESTERN ATLANTIC
Victor E. L. DA SILVA 1*, Elizabeth C. TEIXEIRA 2, Vandick S. BATISTA 2, and Nidia N. FABRÉ 1
1 Laboratory of Ecology, Fish and Fisheries, Federal University of Alagoas, Maceió, Brazil
2 Laboratory of Conservation and Management of Fishery Resources, Federal University of Alagoas, Maceió, Brazil
Da Silva V.E.L., Teixeira E.C., Batista V.S., Fabré N.N. 2018. Spatial distribution of juvenile sh species in
nursery grounds of a tropical coastal area of the south-western Atlantic. Acta Ichthyol. Piscat. 48 (1):
9–18.
Background. Assessing patterns in habitat utilization and changes in the composition of biont assemblages is
a key tool for efcient ecosystem conservation planning and management. Nevertheless, habitat use patterns
by juvenile sh still need more comprehension. Therefore, the presently reported study investigated relations
between the type of nursery ground and the structure of juvenile sh assemblages in a tropical coastal area of the
south-western Atlantic.
Materials and methods. From December 2009 to November 2010, we conducted monthly sampling of
ichthyofauna in two habitat types (mangrove and sandy beach) used as nursery grounds by juvenile sh of the
south-western Atlantic. Species richness and abundance were used to identify spatial and temporal patterns in the
distribution of sh assemblages throughout habitats’ dynamics.
Results. A total of 845 shes representing 16 families and 34 species were found during the presently reported
study: Albula vulpes (Linnaeus, 1758); Atherinella brasiliensis (Quoy et Gaimard, 1825); Strongylura marina
(Walbaum, 1792); Tylosurus acus acus (Lacepède, 1803); Caranx crysos (Mitchill, 1815); Caranx latus Agassiz,
1831; Oligoplites saurus (Bloch et Schneider, 1801); Selene setapinnis (Mitchill, 1815); Selene vomer (Linnaeus,
1758); Centropomus parallelus Poey, 1860; Centropomus undecimalis (Bloch, 1792); Harengula clupeola (Cuvier,
1829); Opisthonema oglinum (Lesueur, 1818); Anchoa tricolor (Spix et Agassiz, 1829); Anchovia clupeoides
(Swainson, 1839); Diapterus auratus Ranzani, 1842; Diapterus rhombeus (Cuvier, 1829); Eucinostomus argenteus
Baird et Girard, 1855; Eucinostomus gula (Quoy et Gaimard, 1824); Eucinostomus melanopterus (Bleeker, 1863);
Bathygobius soporator (Valenciennes, 1837); Conodon nobilis (Linnaeus, 1758); Haemulon plumierii (Lacepède,
1801); Haemulopsis corvinaeformis (Steindachner, 1868); Hemiramphus brasiliensis (Linnaeus, 1758); Lutjanus
apodus (Walbaum, 1792); Lutjanus griseus (Linnaeus, 1758); Lutjanus jocu (Bloch et Schneider, 1801); Mugil
brevirostris (Ribeiro, 1915); Mugil curema Valenciennes, 1836; Mugil curvidens Valenciennes, 1836; Paralichthys
tropicus Ginsburg, 1933; Sphyraena barracuda (Edwards, 1771); Sphoeroides testudineus (Linnaeus, 1758).
No signicant differences in species richness and total abundance were found between habitats and seasons.
Nevertheless, our analyses showed that distinct sets of species use these areas. Moreover, we identied a strong
relation between the rainfall and the species turnover in both habitats studied.
Conclusion. Diversity of nursery grounds in coastal areas not only increases sh diversity but also plays an
important role in the sustaining sh stocks.
Keywords: sh fauna, habitat heterogeneity, mangrove, nursery grounds, sandy beach
ACTA ICHTHYOLOGICA ET PISCATORIA (2018) 48 (1): 9–18 DOI: 10.3750/AIEP/02299
* Correspondence: Victor Emmanuel Lopes da Silva, Universidade Federal de Alagoas, Instituto de Ciências Biológicas e da Saúde, Laboratório de Ecologia, Peixes e
Pesca, Av. Lourival Melo Mota - Tabuleiro do Martins, 57072-900, Alagoas, Brazil, phone: +55 82 9-9843-9768, e-mail: (VELS) lopesdasilvavictor@gmail.com, (ECT)
lizzieteixeira@gmail.com, (VSB) vandickbatista@gmail.com, (NNF) nidia.fabre@pq.cnpq.br.
INTRODUCTION
Coastal and estuarine habitats play an important role in
growth, feeding, and protection of many species (Blaber
and Blaber 1980, Barletta et al. 2005, Vasconcelos et
al. 2010), especially by serving as nursery grounds for
juveniles of marine, freshwater, and estuarine-resident
and brackish-water shes (Beck et al. 2003, Elliott et
al. 2007). Though frequently credited for sustaining sh
stocks (Beck et al. 2003, Crona and Rönnbäck 2007),
these ecosystems, in the global scale, have been impacted
by intense habitat degradation processes, mostly caused
by human activities (Baptista et al. 2015, Freedman et al.
2016). For instance, the transformation of mangrove areas
into shrimp farms along with the shrinking of seagrass
coverage due to water quality degradation and increasing
beach pollution in tropical regions have been vastly
associated with losses of sh diversity and remarkable
declines in shery catches (Arthington et al. 2016). As a
Da Silva et al.
10
result, natural and anthropogenic impacts on coastal biota
have been constantly assessed by ecologists, but some
processes still demand more comprehension, such as the
reciprocal relations between the species and the local
conditions.
Furthermore, our poor understanding of habitat use
patterns by juvenile sh makes conservation planning in
coastal areas a really challenging task (Barletta et al. 2010).
This particular problem can be blamed on numerous studies
that treated these areas as a homogeneous environment,
disregarding their diversity reected by different habitat
types, such as, mangroves, seagrass beds, sandy beaches,
and mudats (Nagelkerken et al. 2000a, Beck et al. 2003,
Minello et al. 2003). Moreover, many eld studies in
these ecosystems are often carried out in single habitats,
because responsible researchers are often discouraged
by their structural complexity, making comparisons of
fauna composition difcult (Nagelkerken et al. 2000a).
This further translates into a lack of reliable information
on the ecosystem as a whole. Mangroves, for instance,
have traditionally received considerable attention from
scientic community due to their distinct features, such
as high structural complexity and greater food abundance
(Vendel and Chaves 2006, Vilar et al. 2011, Castellanos-
Galindo and Krumme 2014). Consequently, other
habitats, especially those without vegetation coverage,
such as coastal sandy beaches, have been less-intensively
investigated (Santos and Nash 1995, Barletta et al. 2010,
Rodrigues and Vieira 2013, Lacerda et al. 2014, Blaber
and Barletta 2016).
Since different coastal habitats have distinct features
(e.g., structural complexity) and dynamics, it is likely that
they may vary in their ecological functions as nursery
grounds (Beck et al. 2003). Thus, identifying patterns
in habitat utilization and changes in the composition
of assemblages is extremely necessary for proper
conservation planning and management of shery
resources in these environments (Barletta et al. 2010,
Blaber and Barletta 2016). In this respect, our study
intended to assess patterns in the distribution of juvenile
sh assemblages in a coastal area from the south-western
Atlantic, considering its availability of nursery grounds
and possible relations between species and environmental
conditions. Specically, we used species richness and sh
abundance to answer the following questions:
Is the structuring of juvenile sh assemblages in these
areas associated with the different types of habitats used
as nursery grounds?
Which (and how) environmental conditions affect the
spatial distribution of juveniles in these habitats?
MATERIALS AND METHODS
Study area and sh sampling. The study was carried out in
the Santo Antônio River estuary (9º24′50′′S, 35º30′24′′W),
located on the north-eastern coast of Brazil, South
America (Fig. 1). The shes were sampled monthly using
a monolament beach seine (15 m wide, 2 m high, and
5-mm mesh size) from December 2009 through November
2010 at four sites located in two different habitat types.
Two sites were situated on the left bank of the estuary,
covered with mangrove forest dominated by Rhizophora
mangle, Avicennia schaueriana, and Laguncularia
racemosa, whereas the other two sites were located in
the shallow waters of a sandy beach (mean depth ≤ 1.5
m) adjacent to the estuary mouth. Each site was sampled
once per month (a total of 48 samples) for 5 min and only
one net type was used to minimize impacts on the existent
fauna. Upon capture, all shes collected were kept on ice.
In the laboratory, each individual was identied to species
level following regional taxonomic keys (Figueiredo and
Menezes 1978, 1980, Menezes and Figueiredo 1980,
1985).
Brazil
9°21’32’’
9°24’29’’
35°30’14’’
35°33’53’’
Atlantic Ocean
Santo Antônio River
Km
0 5
Fig. 1. The Santo Antônio River estuary located on the
north-eastern coast of Brazil, indicating the location of
sampling sites (●)
During eldwork, water physicochemical parameters,
such as salinity [‰], temperature [°C], and dissolved
oxygen [%] were also measured at each site before sh
sampling using a Hanna HI 9828 multi-parameter water
quality portable meter. Monthly rainfall data [mm] were
obtained from the National Institute of Meteorology
(INMET), and these data were used to identify seasonal
trends. The rainy season was dened as the period from
March through August (monthly rainfall 205.4 ± 133.4
mm) and the dry season from September through February
(64.5 ± 63.8 mm).
Data analysis. Non-parametric Kruskal–Wallis test was
used to identify spatial and seasonal patterns in environ-
mental conditions for both habitats since data did not meet
the assumptions of normality and homoscedasticity even
after transformations. Variations in species richness and
total sh abundance were tested between the mangrove
and sandy beach sites, and over time (dry and rainy rea-
son) using two-way analysis of variance (ANOVA). Prior
to analysis, data were log-transformed (lnn + 1) to reduce
the effect of data aggregation. Normality and homoge-
neity of datasets were then tested by Shapiro–Wilk and
Levene’s tests, respectively.
Differences in the composition of sh assemblages
among habitats and seasons were assessed by two-way
analysis of similarity (ANOSIM) using the Bray–Curtis
similarity coefcient (Clarke 1993). To further identify
patterns in assemblages, we also performed a non-metric
Juvenile sh in coastal nursery habitats 11
multidimensional scaling (nMDS) (Anderson and Walsh
2013). Subsequently, species which contributed the most
to the total dissimilarity between samples were identied
using a similarity percentage analysis (SIMPER).
Furthermore, interactions between species abundance
and environmental conditions were investigated by
canonical correspondence analysis (CCA). CCA was
chosen after we tested the gradient length of species
composition by detrended correspondence analysis
(DCA) as suggested by ter Braak (1995). Environmental
variables were previously tested for collinearity by
Pearson’s correlations with a threshold of 0.7 (Dormann
et al. 2012). The two rsts components’ scores and factor
loadings of CCA were then plotted to detect general
gradients in ecological and environmental descriptors.
Additionally, the Monte-Carlo permutation test was used
to determine if the correlations found between species
and environmental conditions were statically signicant.
All analyses were performed in the software R statistics
with the package ‘Vegan’ (Oksanen 2016) at a signicance
level of P < 0.05.
The presently reported study has been carried out in
accordance with Brazilian regulations (Federal Scientic
Fish Sampling Licence 1837810).
RESULTS
The annual precipitation reached 1610 mm and
approximately 76% of this total fell during the rainy
season (March through August). Water temperature and
dissolved oxygen did not differ between habitats and
seasons (P > 0.05). Salinity was higher and stable during
the entire year in the sandy beach (P > 0.05), whereas in the
mangrove a seasonal trend could be observed (P < 0.05).
This trend was characterized by the decreasing of salinity
at the end of the dry season, reaching the lowest values
during the months with high rainfall rates (Fig. 2).
A total of 845 shes representing 16 families and 34
species were collected during the study period: Albula
vulpes (Linnaeus, 1758); Atherinella brasiliensis (Quoy
et Gaimard, 1825); Strongylura marina (Walbaum,
1792); Tylosurus acus acus (Lacepède, 1803); Caranx
crysos (Mitchill, 1815); Caranx latus Agassiz, 1831;
Oligoplites saurus (Bloch et Schneider, 1801); Selene
setapinnis (Mitchill, 1815); Selene vomer (Linnaeus,
1758); Centropomus parallelus Poey, 1860; Centropomus
undecimalis (Bloch, 1792); Harengula clupeola (Cuvier,
1829); Opisthonema oglinum (Lesueur, 1818); Anchoa
tricolor (Spix et Agassiz, 1829); Anchovia clupeoides
(Swainson, 1839); Diapterus auratus Ranzani, 1842;
Diapterus rhombeus (Cuvier, 1829); Eucinostomus
argenteus Baird et Girard, 1855; Eucinostomus gula
(Quoy et Gaimard, 1824); Eucinostomus melanopterus
(Bleeker, 1863); Bathygobius soporator (Valenciennes,
1837); Conodon nobilis (Linnaeus, 1758); Haemulon
plumierii (Lacepède, 1801); Haemulopsis corvinaeformis
(Steindachner, 1868); Hemiramphus brasiliensis
(Linnaeus, 1758); Lutjanus apodus (Walbaum, 1792);
Lutjanus griseus (Linnaeus, 1758); Lutjanus jocu
(Bloch et Schneider, 1801); Mugil brevirostris (Ribeiro,
1915); Mugil curema Valenciennes, 1836; Mugil
curvidens Valenciennes, 1836; Paralichthys tropicus
Ginsburg, 1933; Sphyraena barracuda (Edwards, 1771);
Sphoeroides testudineus (Linnaeus, 1758) (Table 1). Fish
assemblages were mainly comprised of juveniles (73% of
total abundance). In terms of number of individuals, the
most abundant species in the mangrove were Atherinella
24
26
28
30
32
34
Temperature [°C]
0
125
250
375
500
llafniaR ]mm[
0
10
20
30
40
D J F M A M J J A S O N
y [‰]tinilaS
0
10
20
30
40
50
D J F M A M J J A S O N
DO [%]
Months
Mangrove Sandy beach Dry season Rainy season
Fig. 2. Rainfall, mean water temperature, salinity, and dissolved oxygen registered in the mangrove and the sandy beach
studied at the Santo Antônio River estuary during December 2009 and November 2010
Da Silva et al.
12
brasiliensis (20.5%), Mugil curema (17.8%), Caranx latus
(12.2%), Diapterus rhombeus (Cuvier, 1829) (10.5%),
Eucinostomus melanopterus (9.9%), and Anchovia
clupeoides (9.2%), whereas in the sandy beach the most
important species in number, besides A. brasiliensis
(20.9%), C. latus (17.9%), and M. curema (13.5%), were
Mugil curvidens (9.5%), Haemulopsis corvinaeformis
(9.2%), and Hemiramphus brasiliensis (8%). Fourteen of
these species were exclusively found in mangrove sites,
while nine occurred only in the sandy beach area. The eleven
species that occurred in both habitats accounted for 70%
of total abundance. Even though higher species richness
and abundance were registered in some samples from
mangrove than from sandy beach (Fig. 3), no signicant
differences between habitats and among seasons were
found (ANOVA, P > 0.05), neither an interaction between
these two factors could be observed (ANOVA, P > 0.05,
see Table 2 for the total ANOVA output).
While no signicant differences in the structure of
assemblages between seasons were found (ANOSIM, R =
0.16, P > 0.05), sh composition varied signicantly between
mangrove and sandy beach sites (ANOSIM, R = 0.1982,
P < 0.05) (Fig. 4). According to SIMPER analysis, these
differences were partly due to uctuations in the abundance
of common species to both habitats (e.g., Atherinella
brasiliensis and Mugil curema), as well as the exclusive
occurrence of a few species in only one habitat, such as
Diapterus rhombeus in the mangrove and Haemulopsis
corvinaeformis in the sandy beach (Table 3, Fig. 5).
Though some environmental conditions showed a
certain degree of correlation (Table 4), none of them
presented collinearity (R > 0.7), therefore CCA was
performed including all four studied variables. The two
rst axes of CCA explained 65% of total variation in the
relation between species and environmental conditions.
Considering their vectors length and the Monte Carlo
permutation test, rainfall was found to be the most
signicant factor inuencing the distribution and
abundance of most species (Fig. 6, P < 0.05).
DISCUSSION
Many factors can be associated with temporal
and spatial changes in species composition of coastal
environments, such as substrate type (Nagelkerken et
al. 2000b), uctuations in environmental conditions—
especially, salinity, temperature and dissolved oxygen—
(Harrison and Whiteld 2006, Ooi and Chong 2011),
and inter- and intraspecic relations (Elliott et al. 2007).
However, habitat utilization patterns in nursery grounds
are still partly unclear. For example, although mangrove
vegetation coverage is often related to the sustaining
of future sh populations, providing food and shelter
availability for juveniles (Nagelkerken et al. 2001, Beck
et al. 2003, Sales et al. 2016), the absence of signicant
differences in species richness and total sh abundance
reported in our study and in some other earlier works
(Blaber et al. 1989, Sichum and Tantichodok 2013) shows
that non-vegetated areas, such as sandy beaches, are also
suitable environments for several species.
Differences in the composition of juvenile sh
assemblages from mangrove and sandy beach found in
our data indicate that different sets of species use these
areas as nursery grounds. Such variability may result
from specialization in habitat exploitation by species
and by habitat dynamics (Igulu et al. 2014, Ebner et al.
2016). The ability to use different environments within
single ecosystems may depend on species trophic level,
morphological characteristics, and functional attributes
(Matthews et al. 2010, Mouillot et al. 2013, de Andrade
et al. 2015). For instance, sh which inhabit a greater
variety of habitats typically present distinct physiologic
adaptations, intraspecic variability in sh behaviour
(Bourke et al. 1997, Silva-Falcão et al. 2012) and greater
functional specialization and originality (Sales et al.
2016). Our results support this information as species
which were common to both habitats (e.g., Atherinella
brasiliensis and Mugil curema) have been previously
reported in literature as presenting high plasticity in
their diet (Rueda 2002, Contente et al. 2010) and great
tolerance to changes on environmental conditions that are
typical of coastal environments (Neves et al. 2006, Albieri
et al. 2010).
Fluctuations of the environmental conditions are closely
related to the structure of sh assemblages (Blaber et al.
1989, 2010, Harrison and Whiteld 2006) and habitat
selection by species (Porter and Church 1987, Bernardo
0
2
4
6
8
10
Mangrove Beach Mangrove Beach
ssenhcir seicepS
Dry season Rainy season
Sandy
Beach
0
30
60
90
Mangrove Beach Mangrove Beach
ecnadnubA [n rep ]luah
Dry season Rainy season
Sandy
Beach
Sandy
Beach
Sandy
Beach
A
B
Fig. 3. Variation (mean ± SD) in the species richness
(A) and total sh abundance (B) among habitats and
seasons in the Santo Antônio River estuary
Juvenile sh in coastal nursery habitats 13
Table 1
Total number of individuals (n) and relative abundance in percentage (Ab%) of sh species caught in microhabitats
of the Santo Antônio River estuary during December 2009 and November 2010
Family Species Mangrove Sandy beach
nAb% nAb%
Albulidae Albula vulpes — — 14 4.32
Atherinopsidae Atherinella brasiliensis 105 20.5 68 20.9
Belonidae Strongylura marina 30.58 2 0.61
Tylosurus acus acus 1 0.19 — —
Carangidae Caranx crysos 5 0.98 1 0.30
Caranx latus 63 12.3 58 17.9
Oligoplites saurus — — 9 2.77
Selene setapinnis — — 10.30
Selene vomer — — 10.30
Centropomidae Centropomus parallelus 14 2.74 — —
Centropomus undecimalis 71.37 — —
Clupeidae Harengula clupeola — — 15 4.62
Opisthonema oglinum 1 0.19 — —
Engraulidae Anchoa tricolor — — 10.30
Anchovia clupeoides 48 9.41 — —
Gerreidae Diapterus auratus 20.39 — —
Diapterus rhombeus 55 10.7 — —
Eucinostomus argenteus 20.39 13 4.01
Eucinostomus gula 1 0.19 — —
Eucinostomus melanopterus 52 10.1 30.92
Gobiidae Bathygobius soporator 4 0.78 — —
Haemulidae Conodon nobilis — — 10.30
Haemulon plumierii 30.58 — —
Haemulopsis corvinaeformis — — 30 9.25
Hemiramphidae Hemiramphus brasiliensis 5 0.98 26 8.02
Lutjanidae Lutjanus apodus 14 2.74 1 0.30
Lutjanus griseus 30.58 — —
Lutjanus jocu 20.39 — —
Mugilidae Mugil brevirostris 1 0.19 — —
Mugil curema 93 18.2 44 13.5
Mugil curvidens 14 2.74 31 9.56
Paralichthyidae Paralichthys tropicus 1 0.19 — —
Sphyraenidae Sphyraena barracuda — — 10.30
Tetraodontidae Sphoeroides testudineus 11 2.15 4 1.23
Table 2
Two-way ANOVA results for species richness and total abundance of studied sh assemblages
Factor Species richness Total abundance
Df MS F P Df MS F P
Habitat 1 1.44858 2.38 0.1386 15.43127 2.50 0.1284
Season 1 0.05846 0.09 0.7598 1 0.93749 0.43 0.5181
Interaction 1 0.40655 0.66 0.4234 12.13074 0.98 0.3331
Df = degree of freedom, MS = mean sum of squares, F = F-statistic, P = P-value.
et al. 2003). In tropical regions, for instance, variations
in dissolved oxygen, salinity, and rainfall often affect sh
movements and migration, inuencing total density and
biomass (Barletta et al. 2005, Gaonkar et al. 2013, Campbell
and Rice 2014). In our study, rainfall was found to be the
main driver of spatial variability among juvenile sh fauna.
In general, over the studied period, changes in rainfall rate
coincided with remarkable changes in species composition
in both habitats. However, it is important to notice that
rainfall affected their dynamics in different ways.
In the mangrove, the rainfall was negatively correlated
with the salinity, creating a seasonal trend in this habitat.
Many authors have indicated that the salinity was the
main factor structuring sh assemblages in coastal areas
Da Silva et al.
14
(Barletta et al. 2005, Whiteld et al. 2012, Campbell
and Rice 2014). Specically, shifts in the salinity create
a stressful environment for different species which are
typical of these areas (e.g., marine, freshwater, estuarine-
resident and brackish-water species), as each group has
a distinct osmoregulatory capacity (Whiteld et al. 2012,
Telesh et al. 2013), causing species to respond differently
to the salinity gradient. Moreover, the salinity regime
promotes changes in organic matter, nutrients, and in
dissolved and particulate matter, affecting dissolved
oxygen levels (Campbell and Rice 2014) and turbidity
(Barletta et al. 2005). These, in turn, may limit the
abundance and occurrence of many species (de Jonge
and de Jong 2002). On the other hand, since the salinity
did not vary in the sandy beach throughout the year, the
rainfall appeared to be more associated with the discharge
of waters from continental environments, which increases
primary and secondary productivity (Oliveira and Kjerfve
1993, Pereira et al. 2015). Besides, wave actions in these
environments tend to be stronger during the rainy season,
producing a remineralization process of organic matter,
which makes a greater quantity of nutrients in water
column available, also increasing productivity levels
(Rodrigues and Vieira 2013, Lacerda et al. 2014). Greater
food availability increases sh diversity and makes the
environment more suitable for several species, especially
for juveniles which depend on high food availability for
growth (Jones 1986).
In conclusion, the results found herein provide some
insights about the spatial arrangement of juvenile of sh
species in nursery grounds of coastal areas. Our data
suggest that the distinct dynamics of habitats located
in coastal areas allows different sets of species to
inhabit them, not only increasing sh diversity but also
playing a key role in the sustaining of sh stocks. Such
information is supported by the absence of differences in
species richness and total sh abundance in studied sh
assemblages, and by the occurrence of distinct species in
both habitats. Furthermore, we also highlighted rainfall
as the main seasonal factor inuencing, directly and
indirectly, the spatial structuring of juvenile assemblages
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
-0.5 0 0.5
2 etanidrooC
Coordinate 1
Stress: 0.1693
Fig. 4. nMDS ordination of sh abundance per sample
among microhabitats in the Santo Antônio River
estuary; each point represents a sample: ■ mangrove
(rainy season); □ mangrove (dry season); ● sandy beach
(rainy season); ○ sandy beach (dry season)
0
10
20
D J F M A M J J A S O N
A. brasiliensis C. latus M. curema
M. curvidens D. rhombeus H. corvinaeformis
0
20
40
D J F M A M J J A S O N
0
25
50
0
25
50
Mangrove Sandy beach Dry season Rainy season
0
50
100
0
30
60
Months
ecnadnubA [n rep luah ]
D J F M A M J J A S O N
Fig. 5. Fluctuations on the abundance of most common species from the Santo Antônio River estuary
Juvenile sh in coastal nursery habitats 15
in coastal nursery grounds. However, further investigation
on long-term changes in habitats’ dynamics is necessary
for a better understanding of how this variable affects the
sh composition. Our work provides essential data for
understanding shifts in the species composition, which
are extremely necessary for the development of effective
conservation plans for ecosystems as a whole.
ACKNOWLEDGMENTS
We would like to thank colleagues Any Lopes,
Cibele Tiburtino, Jordana Rangely, and Marcia Sousa
for their assistance during eld and laboratory work.
This study was supported by the Brazilian National
Council for Scientic and Technological Development
- CNPq (V.S.B., grant number 303469/2013-7; N.N.F.,
grant number 306624/2014-1); and the Coordination for
the Improvement of Higher Education Personnel - Capes.
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1112.2005.00582.x
Barletta M., Jaureguizar A.J., Baigun C., Fontoura
N.F., Agostinho A.A., Almeida-Val V.M.F., Val
A.L., Torres R.A., Jimenes-Segura L.F., Giarrizzo
Table 3
Species identied by SIMPER analysis as responsible for total dissimilarity between assemblages in the mangrove
and the sandy beach
Species Dissimilarity Mangrove [Ab%] Sandy beach [Ab%]
Contribution [%] Accumulated [%]
Mugil curema 19.55 19.55 15.47 8.85
Atherinella brasiliensis 16.00 35.56 17.86 16.60
Caranx latus 10.56 46.11 10.66 14.30
Mugil curvidens 7.58 53.70 2.33 6.25
Eucinostomus melanopterus 7.23 60.94 8.67 0.65
Hemiramphus brasiliensis 5.23 66.17 0.83 5.45
Diapterus rhombeus 4.93 71.10 9.17 0.00
Anchovia clupeoides 4.89 75.99 8.00 0.00
Haemulopsis corvinaeformis 3.78 79.78 0.00 6.00
Harengula clupeola 3.47 83.25 0.00 3.10
Albula vulpes 3.37 86.63 0.00 3.50
Ab% = relative abundance in percent.
Temperature
Salinity
DO%
Rainfall
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
]%92[ 2ACC
CCA1 [36%]
Fig. 6. Canonical correspondence analysis ordination
bi-plot based on species found in estuarine habitats
(● mangrove; ○ sandy beach; □ both) from the Santo
Antônio River in relation to environmental variables
(arrows; DO% = dissolved oxygen)
Table 4
Pearson correlation matrix for the environmental
variables measured
Dissolved
oxygen
[%]
Rainfall
[mm]
Salinity
[‰]
Temperature
[ºC]
Dissolved
oxygen 1.00
Rainfall –0.23 1.00
Salinity 0.51 –0.61 1.00
Temperature 0.48 –0.20 0.61 1.00
Da Silva et al.
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Received: 29 August 2017
Accepted: 2 January 2018
Published electronically: 31 March 2018
