Effect of water quality variation on fish assemblages in an anthropogenically impacted tropical estuary, Colombian Pacific

Abstract

In tropical estuaries, fish diversity varies spatially and temporally due to behavioral processes such as reproductive migrations, predator avoidance, and foraging, which are affected by water quality. Eutrophication is one of the main factors affecting water quality in estuaries. The objective of this study was to determine variation in fish assemblage explained by fluctuating water quality in the Buenaventura Bay. Fish were captured using artisanal trawl nets during the wet, dry, and transitional seasons at four sampling sites. Additionally, alkalinity; phosphate, nitrite, and nitrate concentrations; dissolved oxygen; pH; temperature; and suspended solids were measured. Multivariate analysis was used to assess the effect of water quality on fish assemblage. In Buenaventura Bay, the assemblage composition of Pseudupeneus grandisquamis, Daector dowi, and Citharichthys gilberti was affected by nitrate concentration. Moreover, large fish biomasses were associated with high nitrite concentration, intermediate salinity, and low dissolved oxygen, suggesting that these estuaries are dominated by species tolerant to poor water quality. Species richness was associated with low nitrate and phosphate concentrations, more suitable water quality indicators, and intermediate temperatures. These results suggest that the deteriorating water quality of estuaries as a result of the anthropogenic impact could increase dominance and decrease richness, resulting in structural changes of fish assemblages.

Full text

/Published online: 30 April 2020
Environmental Science and Pollution Research (2020) 27:25740–25753
RESEARCH ARTICLE
Effect of water quality variation on fish assemblages in an
anthropogenically impacted tropical estuary, Colombian Pacific
Guillermo Duque
1
&Diego Esteban Gamboa-García
2
&Andrés Molina
3
&Pilar Cogua
4
Received: 13 January 2020 /Accepted: 21 April 2020
#The Author(s) 2020
Abstract
In tropical estuaries, fish diversity varies spatially and temporally due to behavioral processes such as reproductive migrations,
predator avoidance, and foraging, which are affected by water quality. Eutrophication is one of the main factors affecting water
quality in estuaries. The objective of this study was to determine variation in fish assemblage explained by fluctuating water
quality in the Buenaventura Bay. Fish were captured using artisanal trawl nets during the wet, dry, and transitional seasons at four
sampling sites. Additionally, alkalinity; phosphate, nitrite, and nitrate concentrations; dissolved oxygen; pH; temperature; and
suspended solids were measured. Multivariate analysis was used to assess the effect of water quality on fish assemblage. In
Buenaventura Bay, the assemblage composition of Pseudupeneus grandisquamis,Daector dowi,andCitharichthys gilberti was
affected by nitrate concentration. Moreover, large fish biomasses were associated with high nitrite concentration, intermediate
salinity, and low dissolved oxygen, suggesting that these estuaries are dominated by species tolerant to poor water quality.
Species richness was associated with low nitrate and phosphate concentrations, more suitable water quality indicators, and
intermediate temperatures. These results suggest that the deteriorating water quality of estuaries as a result of the anthropogenic
impact could increase dominance and decrease richness, resulting in structural changes of fish assemblages.
Keywords Estuarine fish .Tropical estuary .Inorganic pollution .Nitrates .Nitrites .Phosphates .Buenaventura Bay
Introduction
The geographic, biotic, and abiotic factors affect fish richness
and abundance in estuaries (Brown et al. 2007). The geo-
graphic factors include connectivity, while the biotic factors
include reproductive migrations, predator avoidance, and for-
aging (Sheaves et al. 2015) and the abiotic factors include
salinity, temperature, dissolved oxygen, sediments, and nutri-
ents, among others (Menegotto et al. 2019; Rau et al. 2019).
The fluctuation of these physicochemical variables determines
the water quality, influencing the dynamic of aquatic organ-
isms and regulating the ecological processes (Ji 2008).
Water quality of estuarine ecosystems can be characterized
using the concentration ranges of nitrogen, phosphorous, and
dissolved oxygen, among other characteristics, which pro-
mote appropriate ecosystem functioning and support the gen-
eration of ecosystem services (Foley et al. 2015; Pouso et al.
2018). In particular, the Colombian Pacific region is strongly
socioeconomically dependent on the ecosystem services for
the local fish consumption and the commercialization of fish-
ery resources (Saavedra-Díaz et al. 2016; Salas et al. 2019;
Villanueva and Flores-Nava 2019). However, previous studies
Responsible editor: Vedula VSS Sarma
*Guillermo Duque
gduquen@unal.edu.co
Diego Esteban Gamboa-García
degamboag@unal.edu.co
Andrés Molina
aemolinas@unal.edu.co
Pilar Cogua
rosa.cogua00@usc.edu.co
1
Facultad de Ingeniería y Administración, Universidad Nacional de
Colombia Sede Palmira, Palmira, Valle del Cauca, Colombia
2
Facultad de Ciencias Agrarias, Universidad Nacional de Colombia
Sede Palmira, Palmira, Valle del Cauca, Colombia
3
Universidad Nacional de Colombia Sede Caribe, San Andrés y
Providencia, Colombia
4
Facultad de Ciencias Básicas, Universidad Santiago de Cali,
Cali, Valle del Cauca, Colombia
https://doi.org/10.1007/s11356-020-08971-2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

from this region suggest that pollutant concentrations affect
benthic communities (Martínez et al. 2019) and that these
pollutants are bioaccumulating in organisms of commercial
interest (Duque and Cogua 2016; Gamboa-García et al.
2020) as well as in organisms at higher trophic levels
(Gamboa-García et al. 2018b).
Water quality is affected by anthropogenic waste discharge,
which in turn affects pollutant concentrations and physicochem-
ical variables and, ultimately, ecological processes such as the
nutrient cycles, primary production, trophic relationships, and
consumer–species dynamics (Barletta et al. 2019; Jickells et al.
2017;Lemleyetal.2017;Nieetal.2018; Warry et al. 2016). In
particular, the effect of eutrophication of coastal ecosystems
caused by nutrients from rivers and discharge from adjacent
communities on fish assemblages remains unknown.
Eutrophication may positively affect fish assemblage by increas-
ing secondary production through a bottom-up trophic cascade
or may negatively affect fish assemblage by subjecting fish to
physiological stress or hypoxia (de Mutsert et al. 2016; Fong and
Fong 2018; Kenworthy et al. 2016;Nelsonetal.2019; Villafañe
et al. 2017; Wilkerson and Dugdale 2016).
Taking into consideration the multiple potential environ-
mental impacts, it is critical to study estuarine biodiversity
and its dynamics at different scales to understand their pro-
cesses and mechanisms (Duque et al. 2018; França et al. 2011;
Sheaves and Johnston 2009; Teichert et al. 2017; Vilar et al.
2013), as well as to elucidate the effects of eutrophication in
these ecosystems. Species richness, abundance, and fish bio-
mass can be measured to assess the effect of variations in
nutrient concentrations in the ecosystem. Fish biomass, in
particular, may be a key variable because certain species are
sensitive to gaining or losing weight as a result of eutrophica-
tion (de Mutsert et al. 2016), and may affect the total of each
fish population biomass as well.
We hypothesized that (i) the diversity of fishes varies
among sampling seasons and sites, (ii) the abundance of the
most representative fish species of the estuary can be ex-
plained by changes in water quality, and (iii) the fish species
richness and fish biomass are associated with changes in water
quality. The main objective of this study was to assess the
effect of water quality on estuarine fish diversity, which would
enable the evaluation of potential eutrophication in
Buenaventura Bay.
Materials and methods
Study area
This study was carried out in the estuary of Buenaventura Bay
at the Tropical Eastern Pacific (77° 16′Wto3°56′N). The
estuary spans approximately 70 km
2
and has a 16-km-long
and 5-m-deep central canal. The unique seawater inflow is
known as La Bocana and is formed by Punta Bazán in the
north and Punta Soldado in the south, which are approximate-
ly 1.6 km apart (Castaño 2002).
The rivers Dagua (66 m
3
s
−1
)andAnchicayá(98m
3
s
−1
)flow
into this bay (Otero 2004). Moreover, this bay has one of the
highest levels of humidity and precipitation worldwide, with ~
6980 mm of average annual rainfall and two wet seasons (from
September to November and April to June) with an average
monthly rainfall of 567 mm, which represents a significant fresh-
water source (Cantera and Blanco 2001). The access channel for
ships is 9.5 and 11.3 m deep during the low and high tides,
respectively; however, as a result of maintenance dredging activ-
ities and canal expansion, the depth at the channel may reach
more than 16 m (Montenegro and Torres 2016).
In the estuary, there are two well-differentiated zones: the
interior and the exterior bays. Within the interior bay, port
activities combined with (i) waste from fishing, logging, and
mining activities; (ii) discharge from rivers that flow into the
bay; and (iii) domestic discharge from the same municipality
have contributed to the increasing levels of potential pollut-
ants. These pollutants are mainly wastewater which include,
nitrates, nitrites, sulfates, phosphates, and coliforms, increas-
ing the organic matter in both, during the low and high tides
(IIAP 2013). In contrast, the exterior bay is influenced by a
larger touristic complex and it is more marine influenced
(Cantera and Blanco 2001; Palacios and Cantera 2017).
Field sampling
In order to study the different hydroclimatic conditions of the
bay, we conducted three sampling trips at different seasons.
The first one during the wet season (November 2018, total
month precipitation= 753.8 mm), the second one in the dry
season (March 2019, total month precipitation = 321.2 mm),
and the last one in the transitional season (July 2019, total
month precipitation = 469.2 mm) (IDEAM 2020). The four
sampling sites represent a wide range of water salinity, water
quality, nutrient availability, and fish assemblage dynamics.
All samples were taken at sites with water less than 8 m depth.
We sampled four sites within the estuary: The first one going
from the inside of the bay to the outside was the river estuary
(RE, 77° 6′33.1″W and 3° 50′51.5″N), which is the innermost
site and is influenced by the Dagua River that flows into it. This
site (RE) is the closest sampling site to the urban area of
Buenaventura Bay, with around 300,000 inhabitants (DANE
2019). The second site was the inner estuary (IE, 77° 7′24.9″
W and 3° 52′4.4″N), which is also located in the internal estuary
but is characterized for being a little further from the river dis-
charge and the main urban area. The third site was the outer
estuary (OE, 77° 9′35.9″W and 3° 50′58.7″N), which is located
in the external estuary and is characterized by having more
compacted bottoms and further away from the main urban area.
Nevertheless, this site is located near the district of La Bocana,
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Environ Sci Pollut Res (2020) 27:25740–25753
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which is inhabited by approximately 3000 people who are highly
dependent on marine resources for their own consumption, for
tourism (9000 a year approximately), and for supplying the main
urban area markets (Escobar-Cárdenas 2009), which is the largest
portion of the fish landings (no data); The fourth site was the
marine estuary (ME, 77° 12′11.4″W and 3° 49′52.44″N),
which is the outermost site and is more influenced by marine
conditions and has tourism along the year (15,000 approximate-
ly). The average distance between sampling sites was 4 km
(Fig. 1).
During each season and at each site, an artisanal trawling
net was used for sampling with three replicates. Each trawl
sampling lasted 10 min and was performed with a net with
2.54-cm mesh size and 8-m width at the mouth.
Water quality variables, including salinity, dissolved oxy-
gen, pH, temperature, and suspended solids, were measured in
situ using a multi-parameter probe YSI 556 MPS.
Additionally, water samples were collected to determine alka-
linity as well as nitrite, nitrate, and phosphate concentrations
using a portable photometer YSI 9300.
In order to characterize the community structure, the cap-
tured fish were identified to the species level and counted, and
their total length, standard length, and weight were measured.
Fish identification was performed using published taxonomic
keys (Fischer et al. 1995a,b;FroeseandPauly2017;
Marceniuk et al. 2017; Robertson and Allen 2015; Tavera
et al. 2018).
Data analysis
Community structure variations were assessed using species
richness (i.e., number of species) and biomass (in g m
−2
)and
calculated using all the captured fish. Abundanceanalysis was
performed using only the most representative species, which
were selected using the mean of the highest percent frequency
(Eq. 1), abundance (Eq. 2), and weight (Eq. 3)(Martinsetal.
2015).
Frequency ¼100 fish species presence
total fish count

ð1Þ
Abundance ¼100 number of fish by species
total number of fish

ð2Þ
Weight ¼100 individual fish weight by species
total weight

ð3Þ
The spatiotemporal analysis was addressed by calculating
species richness and biomass and checking normality, using
square root transformation when required. Analysis of vari-
ance was performed using season, site, and their interaction as
main factors, and Tukey’s post hoc test was used to examine
statistically significant differences (p<0.05).
In order to assess water quality, the inorganic nitrogen was
measured (nitrites and nitrates, mg L
−1
), inorganic phospho-
rous (phosphates mg L
−1
), and dissolved oxygen (mg L
−1
), as
recommended by Lemley et al. (2015). Moreover, the analysis
included salinity (PSU, practical salinity units), temperature
(°C), and suspended solids (g L
−1
) measurements.
Additionally, the effect of water quality on the abundances
of the most important species (defined by their frequency)
was calculated by a canonical correspondence analysis
(CCA) using the log(x+ 1) transformed matrix within the R
environment (R Core Team 2013).
On the other hand, the variation in species richness and
biomass explained by water quality was calculated by
Fig. 1 Sampling sites within the
Buenaventura Bay estuary: RE =
river estuary, IE= inner estuary,
OE = outer estuary, ME = marine
estuary
25742 Environ Sci Pollut Res (2020) 27:25740–25753
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biological descriptors using Bayesian Generalized Additive
Models (GAMs). The models were evaluated by using differ-
ent variable combinations, and these were compared to select
the best model using Akaike information criterion (AIC). For
each variable, the presented models were selected considering
aΔAIC > 2 between the model and the next lowest AIC
(Krause et al. 2019; Martins et al. 2015). All statistical analy-
ses were performed within the R environment (R Core Team
2013).
It is important to mention that GAMs were used because
traditional statistical methods are difficult to interpret when
the variables have non-linear relationships (Rudy et al.
2016). Moreover, GAMs have been used for a wide range of
applications, including medicine as well as fishery and envi-
ronmental studies, among others areas of research (Amorós
et al. 2018; de Souza et al. 2018; Elith et al. 2008;Tangetal.
2017).
Results
Spatiotemporal variation in fish assemblages
It was collected a total of 69 species belonging to 30 families.
The highest species richness was observed during the transi-
tional season at the RE (20 ± 2) and OE (20 ± 2) (F=10.19,
Tukey’sp< 0.001) sites (Table 1). However, the highest bio-
mass (F= 8.69, Tukey’sp< 0.001) was observed during the
wet season at the ME site (4.4 ± 2.4 g m
−2
)(Table1).
The most representative species (defined using the mean of
frequency, abundance, and biomass) were Sphoeroides
trichocephalus (57.85%), Cathorops multiradiatus
(19.73%), Achirus klunzingeri (18.06%), Lile stolifera
(16.7%), and Pseudupeneus grandisquamis (16.31%). The
highest abundance of Sphoeroides trichocephalus was record-
ed during the transitional season at the RE site (227 ± 14.9
fish) (F=2.16,Tukey’sp< 0.05), whereas the highest abun-
dance of Cathorops multiradiatus was recorded during the
wet season at the ME site (51.7 ± 35.4 fish) (F= 18.76,
Tukey’sp< 0.001). In contrast, the abundance of Achirus
klunzingeri did not show significant differences when the sea-
son and sampling site interaction was analyzed, and abun-
dance was the highest at the OE site (F=5.08, Tukey’sp<
0.01). Similarly, the abundance of Lile stolifera was the
highest during the wet season at the IE site (F= 3.24,
Tukey’sp< 0.05). In addition, the abundance of Achirus
klunzingeri was the highest during the dry season at the OE
site (F= 15.14, Tukey’sp<0.001)(Table2).
Spatiotemporal variation in water quality
In Buenaventura Bay, the highest salinity was recorded during
the dry season (22.24 ± 2.05 PSU), followed by the transition-
al season (21.17± 1.38 PSU) and the wet season (15.83 ± 0.87
PSU) (F= 1212.01, Tukey’sp< 0.001). Moreover, spatial
analysis revealed a salinity gradient, in which salinity was
the lowest in the inner bay (RE and IE) and increased closer
to the sea (OE and ME) (F= 139.51, Tukey’sp< 0.001). The
highest salinity was recorded during the dry season at the ME
site (25.56 ± 0.15 PSU) (F= 31.78, Tukey’sp<0.001)
(Table 3).
The mean water temperature of the Buenaventura Bay was
the highest during the transitional season (28.82± 0.23 °C),
followed by the wet season (28.09 ± 0.10 °C) and the dry
season (27.01± 0.36 °C) (F= 2252.09, Tukey’sp<0.001).
In addition, a spatial pattern was observed, in which the mean
water temperature was the lowest closer to the sea (ME) (F=
96.05, Tukey’sp< 0.001). The highest mean water tempera-
ture was recorded during the transitional season at the RE site
(29.00 ± 0.11 °C), and the lowest water temperature was ver-
ified during the dry season at the ME site (26.42 ± 0.02 °C)
(F= 27.10, Tukey’sp<0.001).
Across all seasons and sampling sites, the highest concen-
tration of dissolved oxygen was recorded during the wet sea-
son at the RE (6.92 ± 0.29 mg L
−1
), IE (7.17 ± 0.39 mg L
−1
),
and OE (7.18 ± 0.37 mg L
−1
)sites(F= 11.41, Tukey’sp<
0.001). Between seasons, the highest concentration of dis-
solved oxygen was recorded during the wet season (6.92±
0.54 mg L
−1
), followed by the dry (5.65 ± 0.67 mg L
−1
)and
the transitional (5.35 ± 0.48 mg L
−1
)(F= 115.06, Tukey’sp<
0.001) seasons. Spatially, the site with the highest dissolved
oxygen concentration was at the ME site (6.31 ± 0.67 mg L
−1
)
(F=6.18,Tukey’sp<0.001)(Table3).
Table 1 Fish species richness and biomass (mean ± standard deviation).
Results from Tukey’s pairwise comparisons (two-way p≤0.05) are
represented with letters for each variable, which are read vertically from
letters a to d. Means were calculated using three replicates. RE,river
estuary; IE, inner estuary; OE, outer estuary; ME, marine estuary
Season Site Fish
Species richness Biomass
Nov 2018 (wet) RE 6 ± 4.6
a
1.2 ± 0.1
abc
IE 13.8 ± 2.9
abcd
1.8 ± 0.5
abcd
OE 9.7 ± 3.2
abc
1.2 ± 0.8
abc
ME 15.7 ± 2.1
bcd
4.2 ± 2.4
d
March 2019 (dry) RE 10 ± 4.0
abc
1.2 ± 1.1
abc
IE 7.7 ± 1.5
ab
0.7 ± 0.1
a
OE 16.7 ± 2.5
cd
2.6 ± 0.8
abcd
ME 6.7 ± 4.0
a
0.7 ± 0.5
ab
July 2019 (transitional) RE 20 ± 2.0
d
3.8 ± 1.1
cd
IE 13.3 ± 3.8
abcd
4.0 ± 0.9
cd
OE 20 ± 2.0
d
3.5 ± 0.9
bcd
ME 7 ± 0.1
ab
0.4 ± 0.2
a
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Considering the season and sampling site interaction, the
highest concentration of nitrates was recorded during the tran-
sitional season at the IE site (2.56± 0.03 mg L
−1
)(F=13.93,
Tukey’sp< 0.001). Among seasons, the highest nitrate con-
centration was recorded during the transitional season (2.15 ±
0.47 mg L
−1
)(F= 92.78, Tukey’sp< 0.001). Spatially, the
highest nitrate concentrations were recorded in the inner zone
(RE = 1.73 ± 0.65 mg L
−1
, IE = 1.56 ± 0.75 mg L
−1
)and
decreased toward the sites closer to the sea (OE = 1.46 ±
0.18 mg L
−1
, ME = 1.41 ± 0.5 mg L
−1
)(F= 5.16, Tukey’s
p< 0.01) (Table 3).
Across all seasons and sampling sites, the highest concen-
tration of nitrites was recorded during the transitional season
at the IE site (0.17 ± 0.01 mg L
−1
)(F= 9.23, Tukey’sp<
0.001). Similarly, nitrite concentration was the highest during
the transitional season (0.13 ± 0.03 mg L
−1
)(F=211.63,
Table 3 Nutrient concentrations across seasons and sampling sites
(mean ± standard deviation). Results from Tukey’spairwise
comparisons (two-way p≤0.05) are represented with letters for each
water quality variable, which are read vertically from letters a to d.
Means were calculated using three replicates. RE, river estuary; IE,
inner estuary; OE, outer estuary; ME, marine estuary. According to
reports on Australian and African estuaries, nutrient eutrophication
levels can be used to determine water quality. Nitrogen: optimal (<
0.1mgL
−1
), low (0.1–1.0 mg L
−1
), and extremely low (>1.0 mg L
−1
);
inorganic phosphorous: optimal (< 0.01 mg L
−1
), low (0.01–0.1mgL
−1
),
and extremely low (> 0.1 mg L
−1
); dissolved oxygen: optimal (>
5mgL
−1
), low (2–5mgL
−1
), and extremely low (<2 mg L
−1
)(Lemley
et al. 2015,2017)
Nitrates
(mg L
−1
)
Nitrites
(mg L
−1
)
Phosphates
(mg L
−1
)
Dissolved Oxygen
(mg L
−1
)
Salinity (PSU) Temperature
(°C)
wet (Nov 2018) RE 1.64 ± 0.17
cd
0.08 ± 0.01
bc
0.18 ± 0.06
b
6.92 ± 0.29
d
14.53 ± 0.27
a
27.97 ± 0.03
c
IE 0.96 ± 0.14
a
0.06 ± 0.02
ab
0.15 ± 0.03
ab
7.17 ± 0.39
d
15.96 ± 0.50
b
28.13 ± 0.08
cd
OE 1.27 ± 0.05
abc
0.03 ± 0.01
a
0.07 ± 0.01
a
7.18 ± 0.37
d
16.16 ± 0.18
b
28.21 ± 0.01
d
ME 1.14 ± 0.21
abc
0.06 ± 0.01
ab
0.08 ± 0.02
a
6.43 ± 0.81
bcd
16.68 ± 0.21
b
28.06 ± 0.02
cd
transitional
(July 2019)
RE 2.52 ± 0.01
ef
0.11 ± 0.02
cd
0.08 ± 0.01
ab
5.48 ± 0.30
abc
21.49 ± 0.87
de
29.00 ± 0.11
f
IE 2.56 ± 0.03
f
0.17 ± 0.01
e
0.08
ab
5.00 ± 0.14
a
20.24 ± 0.83 cd 28.83 ± 0.03
f
OE 1.51 ± 0.13
bcd
0.12 ± 0.02
d
0.13 ± 0.08
ab
4.93 ± 0.11
a
19.95 ± 0.24
c
28.96 ± 0.02
f
ME 2.02 ± 0.35
de
0.10 ± 0.02
cd
0.07 ± 0.02
a
6 ± 0.17
abcd
23.01 ± 0.39
f
28.47 ± 0.09
e
dry (March 2019) RE 1.04 ± 0.03
ab
0.03 ± 0.01
a
0.12 ± 0.01
ab
5.24 ± 0.09
ab
20.54 ± 0.24
cde
27.17 ± 0.07
b
IE 1.18 ± 0.1
abc
0.04 ± 0.01
a
0.15 ± 0.04
a
5.35 ± 0.24
abc
21.32 ± 0.39
de
27.20 ± 0.09
b
OE 1.60 ± 0.17
cd
0.03
a
0.06 ± 0.02
ab
5.51 ± 0.13
abc
21.53 ± 0.13
e
27.23 ± 0.10
b
ME 1.07 ± 0.1
ab
0.03
a
0.08 ± 0.01
ab
6.50 ± 0.94
cd
25.56 ± 0.15
g
26.42 ± 0.02
a
Table 2 Abundance of the most representative fish species of
assemblages (mean ± standard deviation). Results from Tukey’s
pairwise comparisons (two-way p≤0.05) are represented with letters,
which are read vertically from letters a to d, for each fish species.
Means were calculated using three replicates. RE, river estuary; IE,
inner estuary; OE, outer estuary; ME,marineestuary;S_tri,
Sphoeroides trichocephalus;C_mul,Cathorops multiradiatus;A_klu,
Achirus klunzingeri;L_sto,Lile stolifera;P_gra,Pseudupeneus
grandisquamis
Season Site Fish species
S_tri C_mul A_klu L_sto P_gra
Nov 2018 (wet) RE 139 ± 27.8
bcd
1 ± 1.7
a
0.7 ± 1.2 0.7 ± 0.6
ab
0.3 ± 0.6
a
IE 65.7 ± 18.6
ab
0.3 ± 0.6
a
0.7 ± 0.6 9.3 ± 8.4 c 0.7 ± 0.6
ab
OE 51 ± 18.5
ab
2 ± 1.7 a 2.3 ± 0.6
ME 5 ± 2.0
a
51.7 ± 35.4
c
0.3 ± 0.6
March 2019 (dry) RE 78.3 ± 135.7
abc
3.7 ± 2.3
ab
1.3 ± 2.3 2.7 ± 3.1
ab
3.7 ± 0.6
b
IE 23.3 ± 18.5
ab
0.3 ± 0.6
a
2 ± 1.0
ab
OE 91.3 ± 35.2
abc
0.7 ± 0.6 1 ± 1.0
ab
16.3 ± 5.1
c
ME 1 ± 1.7
a
0.3 ± 0.6
a
2.7 ± 3.1
ab
July 2019 (transitional) RE 227 ± 14.9
d
10.7 ± 3.2
bc
2 ± 1.0
a
8.3 ± 8.1
bc
1 ± 1.0
ab
IE 121 ± 112.3
abcd
1.3 ± 1.2 0.3 ± 0.6
a
OE 193 ± 67.6
cd
0.7 ± 1.2
a
1.3 ± 0.6 2.3 ± 2.1
ab
ME 23.3 ± 7.6
ab
0.7 ± 0.6
a
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Tukey’sp< 0.001) and ranged from high concentrations in the
inner estuary (IE = 0.09 ± 0.06 mg L
−1
, RE = 0.074 ±
0.04 mg L
−1
) to low concentration toward the sites closer to
the sea (F= 10.83, Tukey’sp<0.001).
Along the estuary, the highest concentration of phosphates
was recorded during the wet season at the RE site (0.18 ±
0.06 mg L
−1
)(F=4.01, Tukey’sp< 0.01). Although phos-
phate concentration comparisons were not statistically signif-
icant across seasons (F= 1.94), a gradient was detected with
the inner sites presenting higher concentrations (IE = 0.13 ±
0.04 mg L
−1
, RE = 0.12 ± 0.05 mg L
−1
) than the sites closer to
the sea (OE= 0.09± 0.06 mg L
−1
, ME = 0.07 ± 0.01 mg L
−1
)
(F=5.36,Tukey’sp< 0.01) (Table 3).
Effect of water quality variation on the abundance
of the most representative fish species
The canonical correspondence analysis (CCA) suggested that
the distribution of the most representative estuarine fish spe-
cies and the water quality variables nitrites, nitrates, tempera-
ture, and dissolved solids were significantly correlated on the
first and second ordination axes (r=0.86andr= 0.77, respec-
tively), which explained 32% of the variance between fish
species and water quality and physicochemical variables
(Table 4). The results of the permutational testwere significant
(p= 0.001), indicating that the relationships between fish spe-
cies abundance and water quality variables were significant.
The first axis was positively correlated with nitrites and
temperature and negatively correlated with total dissolved
solids, thus representing the temporal gradient of water qual-
ity, with the dry season samples at one end and wet and
transitional season samples at the other (Fig. 2and Table 4).
The second axis was negatively correlated with nitrites, tem-
perature, and pH, thus differentiatingbetween the seasons and
sampling sites with extreme environmental conditions and the
rest of the sampling sites, with the wet season at the ME site
together with the dry season at the RE site at one end and the
rest of the season–site combinations at the other end.
The water quality variables nitrite concentration, tempera-
ture, total dissolved solids, nitrate concentration, and pH sig-
nificantly affected fish assemblage and habitat distribution
comprised by the season–sampling site interactions.
Moreover, the habitats comprised by the interaction of the
transitional season with the OE and IE sites as well as of the
wet season with the IE site displayed the highest nutrient eu-
trophication, pH, and temperature, although no particular fish
assemblage was associated to these environmental conditions.
Conversely, the environmental conditions characteristic of the
interaction between the dry season and the OE, ME, and RE
sites displayed the lowest nutrient eutrophication and temper-
atures but the highest concentration of dissolved solids, and
these conditions were associated with a fish assemblage of
three species (Fig. 2and Table 4).
It was determined a fish assemblage was composed of
Sphoeroides trichocephalus,Lile stolifera,Achirus
klunzingeri,andOphioscion typicus, which plotted close to
the origin of the axes, suggesting that these fish species are
not affected by the water quality gradient (Fig. 2). A second
fish assemblage composed of Pseudupeneus grandisquamis,
Daector dowi,andCitharichthys gilberti was associated with
Fig. 2 Canonical correspondence analysis ordination plot illustrating the
relationships between the abundance of the most representative species
with sampling sites and water quality variables. Thearrows indicate water
quality variables. Alk =alkalinity,DO = dissolved oxygen, Na =nitrates,
Ni = nitrites, P= phosphates, TDS = total dissolved solids, Te m =
temperature. Unfilled circles represent the combination between seasons
(dry = dry season, rain = wet season, and inter = transitional season) and
sampling sites (RE = river estuary, IE = inner estuary, OE =outerestuary,
ME = marine estuary)
Table 4 Canonical correspondence analysis (CCA) of the most repre-
sentative estuarine fish species and water quality variables. The correla-
tions between fish species abundance and water quality variables are
indicated in italics
Total inertia 1.12
model pvalue 0.001
CCA1 CCA2
% of variation explained 20 12
% of variation explained (cumulative) 20 32
Species–environment correlations 0.86 0.77
pvalue 0.001 0.009
Water quality indicator variables pvalue
Nitrites 0.52 −0.62 0.001
Temperature 0.76 −0.46 0.001
Total dissolved solids −0.37 −0.02 0.001
Nitrates 0.22 −0.37 0.04
pH −0.09 −0.53 0.05
Phosphates 0.17 −0.34 0.23
Alkalinity 0.40 0.07 0.24
Dissolved oxygen 0.11 0.09 0.31
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the dry seasons and low nitrite concentration and temperatures
but high total dissolved solid concentrations (Fig. 2). Finally, a
third fish assemblage composed of Cathorops multiradiatus
and Urotrygon rogersi was associated with low nitrite concen-
tration, temperatures, and pH but high dissolved oxygen con-
centrations (Fig. 2).
Effects of water quality on fish species richness
and biomass variation
The totality of the water quality variables was included in the
univariate GAM. The total fish biomass was significantly af-
fected by concentrationof nitrates, nitrites, and total dissolved
solids; salinity; temperature; and dissolved oxygen (p<0.05),
suggesting that each variable affects fish biomass separately
but only accounts for little variation (Table 5). The largest
variation was explained by concentrations of nitrates (43.4%
(Adj. R
2
= 0.25)) and total dissolved solids (31.4% (Adj. R
2
=
0.35)), and salinity (28.9% (Adj. R
2
= 0.23)), respectively. The
model fit for each explanatory variable was low; therefore, a
multivariate analysis was performed to assess the overall ef-
fect on fish biomass.
On the other hand, fish species richness was significantly
and individually affected by temperature; salinity; and con-
centrations of total solids, nitrites, and dissolved oxygen
(p< 0.05) but explained only little variation (Table 5). The
largest variation was explained by temperature (39.5% (Adj.
R
2
= 0.34)) and salinity (30.2% (Adj. R
2
=0.24)). Likewise,
the model fit for each explanatory variable was low; therefore,
a multivariate analysis was performed to assess the overall
effect on fish species richness.
Multivariate analysis showed that the best model for fish
biomass included nitrites and dissolved oxygen concentra-
tions and salinity (AIC = 117.97). This model revealed a
positive relationship between fishbiomass and nitrate concen-
tration and non-linear relationships between salinity (degree =
4) and dissolved oxygen concentration (degree = 2.7) and ex-
plained 64.2% of variation (Adj. R
2
= 0.54). In contrast, the
best multivariate model for fish species richness included ni-
trates, phosphates, temperature, and pH (AIC = 208.17) and
revealed a negative relationship of fish species richness with
phosphate and nitrate concentrations as well as a non-linear
relationship with temperature (degree = 2.7) and explained
61.2% of variation (Adj. R
2
= 0.52) (Table 6).
The total fish biomass showed a positive relationship with
nitrite concentration and a non-linear relationship with salinity
and dissolved oxygen concentrations, peaking around 17 PSU
for salinityand decreasing at 5.5 mg L
−1
for dissolved oxygen
(Fig. 3).
In contrast, fish species richness showed a negative rela-
tionship with nitrate concentration and was the highest at
mean temperatures between 28 and 29 °C but showed no
significant relationship with pH (Fig. 4).
In summary, fish biomass was the largest at higher nitrite
concentrations, intermediate salinities between 16 and
18 PSU, and dissolved oxygen between 5 and 5.5 mg L
−1
,
which were the lowest recorded in this study. However, fish
species richness was the highest at lower nitrate and phosphate
concentrations and temperatures between 28 and 29 °C.
Discussion
Spatiotemporal variation in fish biomass and species
richness
In Buenaventura Bay, the fish assemblages varied across sea-
sons and sampling sites. Among the 69 fish species collected,
Table 5 Results of univariate generalized additive models (GAM)
assessing variation in estuarine fish biomass and species richness.
Model fit (Adj. R
2
), percentage of variation explained by each variable,
as well as the polynomial grade associated with each variable.
Abbreviations:Alk, alkalinity; DO, dissolved oxygen; Na, nitrates; Ni,
nitrites; P, phosphates; TDS, total dissolved solids; Tem , temperature;
Sal, salinity
Smoothing effect S (Na) S (Ni) S (P) S (Alk) S (Sal) S (Tem) S (TDS) S (pH) S (OD)
Biomass edf 4.72 1 1 1 2.92 1.25 3.07 3.22 1.11
F-value 3.32 8.85 0.43 1.04 2.94 7.6 3.13 1.44 4.31
pvalue 0.01* 0.005** 0.52 0.31 0.04* 0.003** 0.03* 0.28 0.03*
Variation explained (%) 43.4 20.7 1.25 2.98 28.9 27.4 31.4 19.4 14.9
Adj. R
2
0.35 0.18 −0.02 0.001 0.23 0.25 0.25 0.11 0.12
Species richness edf 1 1 1 1 2.72 2.94 2.75 2.44 1.08
F-value 2.34 4.14 1.9 0.1 3.62 5.01 3.74 1.2 4.89
pvalue 0.13 0.05* 0.18 0.75 0.02* 0.003** 0.02* 0.35 0.03*
Variation explained (%) 6.43 10.9 5.29 0.3 30.2 39.5 31 14.4 15.4
Adj. R
2
0.04 0.08 0.03 −0.03 0.24 0.34 0.25 0.08 0.13
*p<0.05;**p< 0.01; ***p<0.001
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25 (36%) accounted for 90% of the biomass, 16 (23%) were
present in at least 30% of the trawling events, and 15 (22%)
accounted for 90% of the abundance. The presence of domi-
nant species has been previously reported in other studies in
this region (Molina et al. 2020) as well as in other tropical
estuaries (Castillo-Rivera et al. 2010) and might be explained
by the tolerance of these species to a wide range of environ-
mental conditions characteristic of these ecosystems (Sheaves
et al. 2015).
In this study, the lowest species richness was recorded dur-
ing the wet season in the inner estuary (n= 6, 8.7%) and
during the dry season in the outer estuary (n=7, 10.14%).
The extreme salinities might explain the lower species rich-
ness, as few organisms tolerate these extremes (González-
Sansón et al. 2016). This trend has been previously reported
in other estuaries from the same region, where the lowest fish
species richness was recorded during the seasons with the
lowest salinity (n= 4, 6.3%) (Páez et al. 2018).
Nevertheless, the dominance of some species and the lower
species richness during certain seasons and at some sampling
sites could also be explained by multiple anthropogenic im-
pacts (Fausch et al. 1990; Harrison and Whitfield 2004). In
fact, other anthropogenically impacted estuaries followed sim-
ilar trends. For example, in an Ecuadorian estuary character-
ized by high population density and mangroves disturbed by
shrimp farming, four fish species (12%) accounted for 90% of
the fish abundance (Shervette et al. 2007). Similarly, in a
Mexican coastal lagoon characterized by high population den-
sity and tourist activities, only eight fish species (12.5%)
accounted for 90% of the fish abundance (Páez et al. 2018).
In the Buenaventura Bay, 25 fish species (36%) accounted for
90% of the fish abundance, suggesting that this ecosystem is
resilient to the multiple anthropogenic disturbances.
Nonetheless, dominant species might thrive in highly dis-
turbed estuarine ecosystems, thus threatening biodiversity
within these ecosystems.
On the other hand, the most abundant fish species was
Sphoeroides trichocephalus (Tetraodontidae), in particular
during the transitional season and at sites with contrasting
characteristics: river discharge (RE) and compacted bottoms
(OE). The transitional season corresponds to the July month,
which is one of the periods of highest flux of tourism in the
Dagua basin and Bocana sand beaches (Herrera et al. 2007;
Fig. 3 Effect of water quality
predictor variation on fish
biomass (multivariate analysis:
GAM). Plots represent
relationships indicated by the best
fitting GAM (Tabl e 6).Smoothed
functions are presented as solid
lines; dashed lines denote 2
standard errors
Table 6 Results for multivariate analysis (GAM). Results assessing
variation in estuarine fish biomass and species richness. Number of
species (n), model fit (Adj. R
2
and AIC), percentage of deviance
explained by each model, and the linear coefficient or polynomial grade
associated with each variable. The coefficient is presented between
parentheses and specifies a direction for linear relationships
Biomass Species richness
N36
Adj. R
2
0.54 0.52
Dev. explained (%) 64.2 61.2
AIC 117.97 208.17
Coefficient or polynomial grade
Nitrates ―(−4.9)*
Nitrites 1.21* ―
Phosphates ―(−42.1)*
Alkalinity ――
Salinity 4** ―
Temperature ―2.7***
Total dissolved solids ――
pH ―1.6
DO 2.7 ―
*p<0.05;**p< 0.01; ***p<0.001;―removed during model selection
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Ospina Niño 2017). The Dagua River drains into the RE site,
and Bocana is close to the OE site, which may suggest that
during this time there is an increase in anthropogenic dis-
charges over these areas, which, paradoxically, is related to
higher abundances of S. trichocephalus. Moreover, the wide
environmental distribution of this species may be explained
by the differential niche use of juvenileand adult fish (Velasco
and Wolff 2000). Juvenilesmight benefit from murkier waters
for predator avoidance, while adults might exploit multiple
bottom types for foraging. Finally, S. trichocephalus has been
reported to tolerate extreme environmental conditions, which
allows it to occupy most of the available habitats within the
estuary throughout the year (Molina et al. 2020).
Spatiotemporal variation in water quality
In the studied site, it was recorded a temporal gradient in
which salinity was the highest during the dry season, followed
by the transitional and wet seasons, as well as a spatial gradi-
ent in which salinity was the lowest in the inner sites and
highest in the outer sites. This pattern has been previously
reported in studies in the same bay and was characterized by
salinities below 26 PSU due to high rainfall and runoff from
the Dagua and Anchicayá Rivers (Cantera et al. 1999;
Gamboa-García et al. 2018a,2020;Molinaetal.2020).
Nitrate and nitrite concentrations were the highest during
the transitional season and at the inner sites, whereas phos-
phate concentration was the highest during the wet season and
at the inner sites. During seasons with the highest rainfall,
erosion and runoff increase the discharge of nutrients of the
organic matter from mangroves, as well as the anthropogenic
runoff that flows into river basins (Nie et al. 2018), in this case
the Dagua and Anchicayá Rivers, which might explain the
observed patterns. The Dagua River basin, which includes
the municipalities of Dagua and Buenaventura, is character-
ized by anthropogenic pressures including human settlements,
tourism activities, farming, and the consequent use of fertil-
izers and mining, among others. Moreover, the inner areas of
the estuary are directly affected by domestic wastewater run-
off from the Buenaventura Bay. Additionally, pollution and
mangrove logging impacts have been reported upstream of the
mouth of the Dagua River (Cantera et al. 1999;Romeroetal.
2006), which could have an effect on nutrient cycling in these
ecosystems. This agrees with reports from northern Brazil
(Goiana Riverestuary), where the highest phosphorusconcen-
tration was reported during the season with the highest rainfall
and within the inner estuary (Costa et al. 2017).
In this study, most of the sites and seasons presented a mod-
erate to low water quality. For example, the lowest dissolved
oxygen concentrations were recorded during the transitional sea-
son and at the IE (5.00 ± 0.14 mg L
−1
) and OE (4.93 ± 0.11) sites,
which were classified as moderate. However, these sites were
classified as having low water quality due to their nutrient and
inorganic phosphorus concentrations. These results highlight the
susceptibility to low water quality along the Buenaventura Bay
estuary and during the year. Nevertheless, a study in the Tumaco
Bay (Colombian Pacific, closer to Ecuador) reported that phos-
phate concentration had a range of 0.2 ± 0.1 mg L
−1
and nitrite
plus nitrate concentrations of 1.9 ± 1.8 mg L
−1
(Guzmán et al.
2014), which were similar to the ranges found in Buenaventura
Bay. In that study, a phytoplankton characterization was per-
formed, and an oceanographic analysis, which suggested that
despite the susceptibility of the Tumaco Bay, water quality was
improved by the hydrodynamics of the system, which may flow
away the pollutants. Therefore, the variation in the water quality
and the resilience of the fish community in Buenaventura Bay
Fig. 4 Effect of water quality
predictor variation on fish species
richness (multivariate analysis:
GAM). Plots represent
relationships indicated by the best
fitting GAM (Table 6). Smoothed
functions are presented as solid
lines; dashed lines denote 2 stan-
dard errors
25748 Environ Sci Pollut Res (2020) 27:25740–25753
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may be explained by its hydrodynamic regime, the prominent
tide range, and the high seasonal variation of river flow, which,
similar to Tumaco bay, may improve the ecosystem services.
Effect of water quality on variations in assemblage
of the most representative fish species
In the estuary of Buenaventura Bay, the most representative
fish species were distributed across three assemblages accord-
ing to water quality variables. The dry season on one side, and
wet and transitional seasons on the other, strongly affected fish
assemblage structure. The environmental variables that were
correlated most strongly with the fish assemblages were mean
nitrite concentration and temperature. One assemblage was
composed of Sphoeroides trichocephalus,Lile stolifera,
Achirus klunzingeri,andOphioscion typicus. These species
were recorded across seasons and sampling sites in more than
45% of the trawling events, suggesting that these fish species
are tolerant of water quality variation. This trend has been
previously reported for the estuarine resident species in
Buenaventura Bay (Molina et al. 2020), as well as in other
estuaries around the world (Cabral et al. 2011;Francoetal.
2006;Martinhoetal.2007). In general, species with a wide
physiological tolerance breadth tend to predominate in these
ecosystems (Potter et al. 2015). For example, Sphoeroides
annulatus is classified as euryhaline and tends to dominate a
great proportion of assemblages it is part of (Chávez Sánchez
et al. 2008). Interestingly, this trend has also observed in the
Buenaventura Bay as this species represented the highest
abundance during the wet and transitional seasons and was
tolerant to nutrient concentration variation, dissolved oxygen,
and temperature changes.
On the other hand, a second fish assemblage composed of
Pseudupeneus grandisquamis,Daector dowi,and
Citharichthys gilberti was the most susceptible to nitrite con-
centration and temperature, and was only reported during the
dry season. Previous studies have reported the effect of inor-
ganic nitrogen concentration (Wilkerson and Dugdale 2016)
as well as temperature on fish assemblages (Harrison and
Whitfield 2006;Molinaetal.2020; Rau et al. 2019). Even
though these species were only recorded during the season
with the highest salinity, they were also classified as being
highly dependent on bottom characteristics considering their
movement and foraging behavior (Ramírez-Luna et al. 2008;
Rau et al. 2019). Therefore, the increasing nitrite concentra-
tion in combination with increasing temperatures due to solar
radiation could result in a bottom-up nutrient control, which
could increase the eutrophication conditions and negatively
affect the fish assemblages. Moreover, increased nitrite con-
centration (Camargo and Alonso 2006; Schlacher et al. 2007)
and water temperature (Jeffries et al. 2016; Madeira et al.
2016) could represent physiologically stressful surroundings
for fishes.
Furthermore, a third fish assemblage of fishes composed of
Cathorops multiradiatus and Urotrygon rogersi was recorded
during the wet season in the outer bay and was associated with
low pH and high dissolved oxygen concentrations, which is
characteristic of the runoff of the rivers Dagua and Anchicayá
(Cantera and Blanco 2001). The distribution of species
forming this assemblage is consistent with that reported in
previous studies from this region (Castellanos-Galindo et al.
2006;Molinaetal.2020). Moreover, the highest abundance of
Cathorops multiradiatus and Urotrygon rogersi recorded in
the outer estuary region might be explained by the runoff of
the rivers that creates environmental conditions to adjacent
waters, facilitating resource provisioning to the most marine
species (Elliott et al. 2007;Potteretal.2015;Molinaetal.
2020). Finally, fish assemblages varied mostly temporally as
an effect of nitrite concentration and temperature, suggesting
that water quality and estuarine ecosystem services in the
Buenaventura Bay are susceptible to eutrophication and
highlighting the complexity and ecological relevance of the
processes.
Effect of water quality on fish species richness
and biomass variation
In the estuary of Buenaventura Bay, fish species richness and
biomass were affected by water quality. Higher biomasses
were recorded in low-quality waters, enriched with nitrites
and with a low dissolved oxygen. This trend was reported
during a hypoxia event in a Mexican estuary, where the fish
species representing the highest biomass benefited from the
bottom-up effect as a result of primary and secondary produc-
tion and could also tolerate low dissolved oxygen concentra-
tions, which allowed themto avoid predators (de Mutsert et al.
2016). In summary, in the Buenaventura Bay, the increased
fish biomass and dominance of certain species could be an
indicator of the effect of low water quality.
In contrast, fish species richness was the highest at intermedi-
ate salinities, which is consistent with results from the Málaga
Bay, an adjacent estuary to the Buenaventura Bay, where the
highest species richness was recorded at intermediate salinities
(Castellanos-Galindo and Krumme 2015). These open estuaries
are characterized by a wide salinityrange:low-salinityhabitats
(under 10 PSU) that are unsuitable for marine fish in some re-
ported estuaries (Martino and Able 2003), as well as
intermediate-salinity habitats. Thus, these relatively intermediate
salinities could provide a salinity ecotone, which could be toler-
ated by resident estuarine species as well as marine species that
also depend on the estuary ontogenetically.
Furthermore, a low species richness was reported in
low-quality waters, characterized by high nitrate and
phosphate concentrations. Increased nitrate and phos-
phate concentrations have been previously linked to an-
thropogenic activities (Camargo and Alonso 2006;
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Smith 2003; Wilkerson and Dugdale 2016), such as
wastewater runoff from urban settlements into estuaries,
which in turn affects ecological cycles (Berbel et al.
2015). This suggests that the presence of these nutrients
indicates a disturbed and low-quality habitat as a result
of urban wastewater runoff, which contains nutrients as
well as other pollutants. Consequently, fish could suffer
from pathologies of different organs, such as the gills,
liver, and kidney, or become more susceptible to para-
sites (Schlacher et al. 2007). Moreover, this could affect
their vitality, affecting community structure and ecosys-
tem functioning. Thus, the effect of anthropogenic nu-
trient runoff on fish assemblages may be evident in
Buenaventura Bay, particularly in relation to nitrates,
as this nutrient presented the highest concentrations in
most of the estuary.
Considering the socioeconomic importance of the
Buenaventura Bay estuary for the region for tourism
and fishing for livelihood and commercial purposes, it
is critical to monitor, control, and treat anthropogenic
runoff that might flow into the estuary. In addition,
the relevant authorities should develop initiatives to
monitor and assess septic tanks from rural communities
and tourist centers adjacent to the sea. This study high-
lights the importance of assessing inorganic pollution
within estuaries, and future studies should complement
this with histopathology of fish and its potential effect
on human health, in addition, chlorophyll-a and micro-
biological analyses. Moreover, fish assemblages could
be used as ecosystem functioning indicators, and certain
fish populations should be permanent monitored.
Acknowledgments We thank the Universidad Nacional de Colombia for
their administrative support. We also thank the Ecología y Contaminación
Acuática research group for their support in the field, sample processing,
and data analysis.
Funding information We received from the Universidad Nacional de
Colombia financial support of the project: “Efectos de los cambios en la
calidad del agua en las comunidades de macroinvertebrados y peces del
estuario Bahía de Buenaventura”código Hermes 42118. We also received
from the Universidad Santiago de Cali financial support of the project:
“Fortalecimiento de grupos de investigación”código: 934-621118-204.
Data availability The data in this study are available upon reasonable
request to the corresponding author.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interest.
Ethics approval The methods in this study were approved by the ethics
committee of the Environmental Studies Institute (Spanish acronym:
IDEA) of the Universidad Nacional de Colombia and follow internation-
al, national, and institutional animal use and care guidelines.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, pro-
vide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included
in the article's CreativeCommons licence, unless indicated otherwise in a
credit line to the material. If material is not included in the article's
Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this
licence, visit http://creativecommons.org/licenses/by/4.0/.
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