UNIVERSIDADE FEDERAL DO PARANÁ THAYANNE LIMA BARROS ESTRESSE OXIDATIVO NO POLIQUETA LAEONEREIS CULVERI APÓS CONTAMINAÇÃO E DESCONTAMINAÇÃO ABRUPTAS POR ESGOTO EM UM ESTUÁRIO SUBTROPICAL OXIDATIVE STRESS IN THE POLYCHAETE LAEONEREIS CULVERI AFTER ABRUPT CONTAMINATION AND DECONTAMINATION BY SEWAGE IN A SUBTROPICAL ESTUARY CURITIBA 2016
36
Embed
universidade federal do paraná thayanne lima barros estresse ...
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
UNIVERSIDADE FEDERAL DO PARANÁ
THAYANNE LIMA BARROS
ESTRESSE OXIDATIVO NO POLIQUETA LAEONEREIS CULVERI APÓS
CONTAMINAÇÃO E DESCONTAMINAÇÃO ABRUPTAS POR ESGOTO EM
UM ESTUÁRIO SUBTROPICAL
OXIDATIVE STRESS IN THE POLYCHAETE LAEONEREIS CULVERI AFTER
ABRUPT CONTAMINATION AND DECONTAMINATION BY SEWAGE IN A
SUBTROPICAL ESTUARY
CURITIBA
2016
2
UNIVERSIDADE FEDERAL DO PARANÁ
THAYANNE LIMA BARROS
ESTRESSE OXIDATIVO NO POLIQUETA LAEONEREIS CULVERI APÓS
CONTAMINAÇÃO E DESCONTAMINAÇÃO ABRUPTAS POR ESGOTO EM
UM ESTUÁRIO SUBTROPICAL
OXIDATIVE STRESS IN THE POLYCHAETE LAEONEREIS CULVERI AFTER
ABRUPT CONTAMINATION AND DECONTAMINATION BY SEWAGE IN A
SUBTROPICAL ESTUARY
CURITIBA
2016
1
UNIVERSIDADE FEDERAL DO PARANÁ
THAYANNE LIMA BARROS
ESTRESSE OXIDATIVO NO POLIQUETA LAEONEREIS CULVERI APÓS
CONTAMINAÇÃO E DESCONTAMINAÇÃO ABRUPTAS POR ESGOTO EM
UM ESTUÁRIO SUBTROPICAL
OXIDATIVE STRESS IN THE POLYCHAETE LAEONEREIS CULVERI AFTER
ABRUPT CONTAMINATION AND DECONTAMINATION BY SEWAGE IN A
SUBTROPICAL ESTUARY
Dissertação apresentada ao Curso de
Pós-Graduação em Zoologia, Setor de
Ciências Biológicas da Universidade
Federal do Paraná.
Orientador: Dr. Paulo da Cunha Lana
CURITIBA
2016
2
3
AGRADECIMENTOS
Ao Paulo, pela dedicação, acessibilidade e principalmente pelo entusiasmo
durante esses dois anos.
Aos professores José Monserrat, Helena de Assis e Afonso Bainy, pela
disponibilidade e interesse em compor a banca avaliadora dessa dissertação.
Ao Centro de Estudos do Mar e à Universidade Federal do Paraná (UFPR),
pelo apoio logístico e infraestrutura para realização das atividades.
Ao Programa de Pós-Graduação em Zoologia da Universidade Federal do
Paraná, ao Conselho Nacional de Desenvolvimento Científico e Tecnológico
(CNPq) e à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
(CAPES), pela concessão da bolsa de mestrado.
Ao professor Dr. Adalto Bianchini e à Dra. Roberta Klein do Instituto de
Ciências Biológicas FURG, pela recepção e atenção durante minha estadia na
FURG e pelo aprendizado das técnicas e procedimentos para determinação
dos biomarcadores utilizados neste trabalho.
À Mariana Holanda e ao Lucas Maltez, pela calorosa acolhida durante o
período das análises em Rio Grande.
Ao professor Dr. César Martins do Laboratório de Geoquímica Orgânica e
Poluição Marinha do CEM/UFPR pelas análises geoquímicas, à Ana Lucia
Lindroth Dauner pela extração das amostras e Ana Caroline Cabral pela
análise cromatográfica.
Ao professor Dr. Marcelo Lamour e à Msc. Pâmela Cattani do Laboratório de
Estudos Morfodinâmicos e Sedimentológicos do CEM/UFPR, pelas análises
granulométricas.
À professora Dra. Helena Cristina da Silva de Assis e à Dra. Izonete Guiloski
do Departamento de Farmacologia da UFPR, pelo armazenamento das
amostras.
Aos motoristas, vigilantes noturnos, marinheiros, telefonista, técnicos e demais
funcionários do Centro de Estudos do Mar por toda ajuda e parceria durante o
desenvolvimento do projeto. Um agradecimento especial ao Agnaldo,
Alexandre, Fumaça, Edinaldo, Pedro, Abraão, Felipe, Josias, Moisés, Ronei e
Sérgio.
Aos professores do Programa de Pós-Graduação em Zoologia e em Sistemas
Costeiros e Oceânicos da UFPR, pelos ensinamentos e contribuições
acadêmicas.
4
Aos integrantes do Laboratório de Bentos pelo companheirismo, “pitacos” e
ajuda durante os experimentos.
Aos voluntários que ajudaram nas inúmeras idas à campo, triagens, testes,
montagem de equipamento, instalação dos experimentos e organização das
5.49 6.13 0.55 0.56 >50%: high sewage contamination
epicoprostanol/coprostanol
0.20 0.11 Nd Nd <0.20: untreated sewage input
3.2. Acute Experiment
ACAP levels varied significantly for the interaction among treatments,
times and blocks (interaction Tr x T x B) on both manipulations (UC to C and C
to UC), as demonstrated by the ANOVA test (supplementary material). A
posteriori analysis revealed that significant differences were only observed in
block 1. SNK tests revealed that the means of ACAP levels were higher in
17
control from contaminated than in control from uncontaminated areas.
Variations in MDA levels were significant between Treatment and Time (UC to
C) and between Treatment and Block (C to UC; Fig. 3). Nonetheless, in both
analyses, sampling times constituted a very large source of variation (note the
sizes of mean squares and F values for this term in supplementary material
Table 4).
Fig. 3 Acute experiment. Mean values of lipid peroxidation (MDA) and antioxidant competence
against peroxyl radicals (ACAP) in the polychaeta Laeonereis culveri in response to reciprocal
transplantation between a contaminated and uncontaminated estuaries. UC = uncontaminated
area, C= contaminated area, Cuc = control for the uncontaminated area, Cc = control for the
contaminated area, TLc = translocate in the contaminated area TPc = transplant to the
contaminated area, TLuc = translocate from the uncontaminated, TPuc = transplant to the
uncontaminated area, B1 = block 1, B2 = block 2. For Student–Newman–Keuls (SNK) tests, the
mean values are listed in ascending order. ‘‘>’’ indicates p < 0.05 and ‘‘=’’ indicates p > 0.05. "*"
denotes significant difference by SNK procedure. Lower values of ACAP data indicate lower
antioxidant capacity.
18
3.2.1. Transplant to the contaminated area (UC to C)
Variations in MDA levels were significantly affected by the interaction
between treatment and time (interaction Tr x T; F = 4.88; P < 0.05). The SNK
test and comparisons between the treatments revealed that MDA levels of
control from uncontaminated area were significantly lower than in the others
treatments at T3 (96 hours) (Cuc<TLc=Cc=TPc; Fig. 3).
Significant differences in ACAP levels resulted from the combined effect of
the treatments, time and sites (interaction Tr x T x B; F = 3.1576; P < 0.05).
ACAP levels on translocate treatment were significant higher than in the other
treatments (Cuc=Cc=Tpc<TLc) on time 2, block 1. At the same site on time 3
the ACAP levels were significantly higher in control from the contaminated area
(Cuc=TPc=TLc<Cc; Fig. 3).
3.2.2. Transplant to the uncontaminated area (C to UC)
The experimental manipulation did not cause detectable short-term changes
in the levels of MDA. Variations in MDA levels were significantly affected by the
combined effects of time and blocks (interaction T x B; F = 6.3974; P < 0.01 ).
On block 1, SNK a posteriori comparisons showed that MDA levels were
significantly lower at time 1 with not significant differences between T2 and T3
(T1<T3=T2) whereas on block 2, time 1 has once again the lower mean value
but time 2 shows higher MDA levels than T3 (T1<T3<T2; Fig. 3).
Variations in the mean values of ACAP radical levels resulted from the
combined effect of the treatments, time and sites (interaction Tr x T x B; F =
5.08; P < 0.001). ACAP levels of control from contaminated area were
significantly higher than the other treatments on times 2 and 3 in block 1
(Cuc=TLuc=Tpuc<Cc; TPuc=Cuc=TLuc<Cc; Fig. 3).
3.3. Chronic experiment
The mean values of total antioxidant capacity against peroxyl radical levels
were significantly affected by the combined effects of treatment, time and sites
(interaction Tr x T x B). Variations in MDA levels resulted from the combined
effect of the treatment and the blocks (interaction Tr x B), on both manipulations
(UC to C and C to UC) and sampling times constituted a very large source of
variation (note the sizes of mean squares and F values for this term in
supplementary material Table 5).
3.3.1. Transplant to the contaminated area (UC to C)
MDA levels varied significantly among treatments according to blocks and
time (Tr x B and Tr x T;F = 5.6325 and 12.973; P < 0.01 and P < 0.05,
respectively). SNK a posteriori comparisons showed that the MDA levels of
control from uncontaminated area was significantly higher on block 2
19
Fig. 4. Chronic experiment. Mean values of lipid peroxidation (MDA) and antioxidant
competence against peroxyl radicals (ACAP) in the polychaeta Laeonereis culveri in response
to reciprocal transplantation between a contaminated and uncontaminated estuaries. UC =
uncontaminated area, C= contaminated area, Cuc = control for the uncontaminated area, Cc =
control for the contaminated area, TLc = translocate in the contaminated area TPc = transplant
to the contaminated area, TLuc = translocate from the uncontaminated, TPuc = transplant to the
uncontaminated area, B1 =block 1, B2 = block 2. For Student–Newman–Keuls (SNK) tests, the
mean values are listed in ascending order. ‘‘>’’ indicates p < 0.05 and ‘‘=’’ indicates p > 0.05. "*"
denotes significant difference by SNK procedure. Lower values of ACAP data indicate lower
antioxidant capacity.
(TLc=Cc=TPc<Cuc; Fig. 4). There was no significant difference between
treatments on time 1 (TLc=Cc=TPc=Cuc). The MDA levels were higher in
control from uncontaminated area and transplant to the contaminated area than
in control from contaminated area and translocate of contaminated area
(TLc=Cc<Cuc=TPc) on time 2 (Fig. 4).
20
Variations on ACAP levels resulted from the combined effect of the
treatment, time and sites (interaction Tr x T x B; F = 10.8774; P < 0.001). The
SNK test and comparisons revealed that such variations occurred only in block
1. The mean values of ACAP were significantly lower in control from
contaminated area than in transplant to the contaminated area, and the mean
values of ACAP were significantly higher in control from uncontaminated area
than in translocated of contaminated area and transplant to the contaminated
area (Cc<TPc=TLc<Cuc; Fig. 4).
3.3.2. Transplant to the uncontaminated area (C to UC)
Variations in MDA levels resulted from the combined effects from the
treatment with the block and the time with the block (Tr x B and T x B, F =
4.3593 and 5.8405; P < 0.05, respectively). The SNK test and comparisons
showed that the mean values of MDA levels were significantly higher on time 2
than in time 1 on both blocks (T1<T2;Fig. 4).
Variations on ACAP were significantly affected by the combined effects of
treatment, time and sites (interaction Tr x T x B; F = 10.1675; P < 0.001). There
was no significant difference between the mean values of ACAP levels of
treatments on time 2 on both blocks. On time 1, the mean value of ACAP was
significantly higher in control from uncontaminated area than in the other
treatments on block 1 (Cc=TPuc=TLuc<Cuc) and significantly lower on block 2
(Cuc<Cc =TLuc=TPuc; Fig. 4).
4. DISCUSSION
We refuted the hypothesis that reciprocal experimental transplantation
induces acute or medium-term variation in oxidative stress responses of the
worm Laeonereis culveri. With the exception of a short-term response of MDA
levels after the abrupt exposure to sediment contaminated by domestic sewage
effluents, none of the biochemical responses were significantly affected by
reciprocal sediment transplants between contaminated and uncontaminated
areas
Differences in faecal and total sterol concentrations between blocks
indicate that the experimental blocks were not homogeneous, contrary to what
was expected. The local distribution of faecal sterols typically follows a gradient
pattern caused by the proximity to the sewage outfall (Barboza et al., 2015;
Martins et al., 2014). Barboza et al., (2015) showed a clear contamination
gradient at the kilometer scale at the study area. However, it is also known that
faecal sterol concentrations can dramatically decrease at small spatial scales
(Martins et al., 2002) or not necessarily be higher close to sewage outfalls, due
to dispersion processes (Mudge and Duce, 2005). In this context, our results
show that the 40 m distance between blocks at the contaminated area was
21
enough to considerably decrease sterol levels, probably as a consequence of
quick dispersion and dilution of the sewage-discharge plume. Discrepancy in
sterol distributions and concentrations can also be explained by small-scale
differences in sediment texture (Biache and Philp, 2013) . However, sediment
texture was similar between our experimental blocks. Thus, our results shows
significant variation in contamination conditions even at small spatial scales,
furthermore stressing the relevance of robust spatial replication for
ecotoxicological monitoring programs.
In the acute assay, as expected, the abrupt exposure to sediment
contaminated by domestic sewage effluents induced a short-term increase in
MDA levels. However, there was no significant decrease in ACAP levels
between transplanted worms and those from origin habitats. There was no
significant short-term decrease of MDA levels or increase of ACAP levels in the
transplant from a contaminated to an uncontaminated area. However, ACAP
levels of transplanted organisms decreased over time.
During the acute experiment, ACAP levels showed to significantly vary
only in block 1 for both transplants scenarios. Such a marked heterogeneity in
local contamination conditions may partially explain the unexpected response
patterns in the contaminated area, with significant short-term responses only
detected in block 1 for both transplant scenarios. Based on that, all the following
discussion on variations in ACAP refer only to the block 1.
Higher MDA levels in the control from contaminated area, transplanted
and translocated treatments (Cuc<TLc=Cc=TPc) on time 3 in transplants to the
contaminated area (UC to C) indicates that the antioxidant defense was not
sufficient to prevent oxidative damage at the lipid level. Four days of exposure
to sewage were enough to induce lipid peroxidation in transplanted organisms.
The lower mean values of antioxidant capacity in the transplanted, translocated
and control group from uncontaminated area (Cuc=TPc=TLc<Cc) on time 3,
indicate a potential higher susceptibility to oxidative damage by specific
oxyradicals. The pro-oxidant effect of sewage pollution promoting lipid
peroxidation has been previously reported for several other aquatic species
(Bianchi et al., 2014; López-López et al., 2006; Maranho et al., 2015; Soorya et
al., 2013; Vlahogianni et al., 2007).
Transplant to the uncontaminated area (C to UC) did not induce any
oxidative damage at the lipid level, as expected, since the organisms were
transplanted to an area with less anthropogenic pressure (Lukyanova, 2006;
Rocchetta et al., 2014). There were no significant difference between ACAP
levels of transplanted organisms and control of uncontaminated area after 48
hours. This indicates that organisms adapted to new environmental conditions
after an abrupt decontamination (less than 48-hours). Quick recovery responses
22
after abrupt sewage decontamination were also reported by Ferreira et al.,
(2005) and Liu et al. (2011) in fish and nematode community, respectively.
The ANOVA and SNK tests In the chronic assay indicated that variations
in biochemical responses were more related to background variability over time
and heterogeneity among areas than to the experimental manipulation itself. In
a previous experiment conducted with Perinereis gualpensis, at a same time
scale, the total antioxidant capacity and lipid peroxidation levels did not
consistently reflect differences between sites under different anthropogenic
pressure (Díaz-Jaramillo et al., 2013). The lack of differences between
treatments confirms that medium-term responses was attributed rather to the
high environmental variability than to experimental manipulation. This strongly
suggests that organism recovery after an abrupt contamination and
decontamination is a short-term process, which occurs on the scale of hours or
less than 4 days. However, Geracitano et al. (2004) showed different patterns
for biomarkers response in Laeonereis acuta after copper exposure, with
significant responses at medium but not short-time scales.
Variations on ACAP levels in 48 hours were probably a methodological
artefact, (note the high level in supplementary material and the standard error in
Figure 3).
Significant differences in ACAP levels between translocated and control
treatments, detected during the chronic experiment, were more related to
heterogeneity among experimental blocks than to methodological artefacts,
since there was a difference in faecal and total sterol concentrations between
blocks.
ACAP levels were higher in organisms from contaminated than in those
from uncontaminated areas in the acute experiment. An inverse pattern was
detected in the chronic experiment, where ACAP levels were lower in
organisms from contaminated than in those from uncontaminated areas (except
in block 2 time 1). This is unexpected, since the capacity to face oxidative stress
have previously proved to be lower in organisms from contaminated coastal
areas (Machado et al., 2014; Díaz-Jaramillo et al., 2010; Ferreira-Cravo et al.,
2007; Geracitano et al., 2004). Different response patterns for ACAP were also
recorded by Díaz-Jaramillo et al. (2011) and Díaz-Jaramillo et al. (2013).
Probably, ACAP response to xenobiotics does not follow a specific pattern,
especially when results of field and laboratory are compared. Therefore, such
comparisons should be made cautiously because organisms are subject to
variables and scales of variability that are quite different under each condition.
There is clearly a demand for more complex ecological assessments and
a further advance for ecotoxicology will dependent upon better integration of lab
toxicology with field experiments. For this, field, laboratory studies and long-
23
term monitoring combined with an advanced understanding of larger and more
complex variation along time and spatial scales are necessary to ensure that
the ecological complexity will be taken into account for better management
strategies and environmental monitoring programs.
5. CONCLUSIONS
There were no medium-term significant responses after the transplants
between contaminated and uncontaminated areas, as shown by the response
of oxidative stress and antioxidants biomarkers. With the exception of a short-
term response of MDA levels after the abrupt exposure to sediment
contaminated by domestic sewage effluents, none of the biochemical responses
were significantly altered by the impact. This result was attributed to the high
environmental variability between the experimental sites. Nevertheless, our
results strongly suggests that recovery in L. culveri after an abrupt
contamination and decontamination occurs on a very short term scale,
indicating the resilience or ability of fast recovery in estuarine species.
We also stress the importance of robust spatial replication for
ecotoxicological monitoring programs (as blocks were always interacting with
the other factors and showing significant differences), mainly when information
is scant about the local fauna and about the background variability of
biomarkers.
6. REFERENCES
Abreu-Mota, M.A., de Moura Barboza, C.A., Bícego, M.C., Martins, C.C., 2014. Sedimentary biomarkers along a contamination gradient in a human-impacted sub-estuary in Southern Brazil: A multi-parameter approach based on spatial and seasonal variability. Chemosphere 103, 156–163.
Almeida, E.A., Bainy, A.C.D., Dafre, A.L., Gomes, O.F., Medeiros, M.H.G., Di Mascio, P., 2005. Oxidative stress in digestive gland and gill of the brown mussel (Perna perna) exposed to air and resubmersed. J. Exp. Mar. Bio. Ecol. 318, 21–30.
Amado, L.L., Garcia, M.L., Ramos, P.B., Freitas, R.F., Zafalon, B., Ferreira, J.L.R., Yunes, J.S., Monserrat, J.M., 2009. A method to measure total antioxidant capacity against peroxyl radicals in aquatic organisms: Application to evaluate microcystins toxicity. Sci. Total Environ. 407, 2115–2123.
Barboza, C.A. de M., Martins, C.C., Lana, P. da C., 2015. Dissecting the distribution of brittle stars along a sewage pollution gradient indicated by organic markers. Mar. Pollut. Bull. 100, 438–444.
Baussant, T., Bechmann, R.K., Taban, I.C., Larsen, B.K., Tandberg, a H., Bjørnstad, a, Torgrimsen, S., Naevdal, a, Øysaed, K.B., Jonsson, G., Sanni, S., 2009. Enzymatic and cellular responses in relation to body burden of PAHs in bivalve molluscs: a case study with chronic levels of
24
North Sea and Barents Sea dispersed oil. Mar. Pollut. Bull. 58, 1796–807.
Bebianno, M.J., Company, R., Serafim, A., Camus, L., Cosson, R.P., Fiala-Médoni, A., 2005. Antioxidant systems and lipid peroxidation in Bathymodiolus azoricus from Mid-Atlantic Ridge hydrothermal vent fields. Aquat. Toxicol. 75, 354–73.
Bevilacqua, S., Terlizzi, A., Fraschetti, S., Russo, G.F., Boero, F., 2006. Mitigating human disturbance: Can protection influence trajectories of recovery in benthic assemblages? J. Anim. Ecol. 75, 908–920.
Biache, C., Philp, R.P., 2013. The use of sterol distributions combined with compound specific isotope analyses as a tool to identify the origin of fecal contamination in rivers. Water Res. 47, 1201–1208.
Bianchi, V.A., Rocchetta, I., Luquet, C.M., 2014. Biomarker responses to sewage pollution in freshwater mussels ( Diplodon chilensis ) transplanted to a Patagonian river. J. Environ. Sci. Heal. Part A 49, 1276–1285.
Bocchetti, R., Regoli, F., 2006. Seasonal variability of oxidative biomarkers, lysosomal parameters, metallothioneins and peroxisomal enzymes in the Mediterranean mussel Mytilus galloprovincialis from Adriatic Sea. Chemosphere 65, 913–921.
Brauko, K.M., Souza, F.M., Muniz, P., Camargo, M.G., Lana, P.C., 2015. Spatial variability of three benthic indices for marine quality assessment in a subtropical estuary of Southern Brazil. Mar. Pollut. Bull. 91, 454–460.
Burton, G.A., Greenberg, M.S., Rowland, C.D., Irvine, C. a., Lavoie, D.R., Brooker, J. a., Moore, L., Raymer, D.F.N., McWilliam, R. a., 2005. In situ exposures using caged organisms: A multi-compartment approach to detect aquatic toxicity and bioaccumulation. Environ. Pollut. 134, 133–144.
Cajaraville, M.P., Bebianno, M.J., Blasco, J., Porte, C., Sarasquete, C., Viarengo, A., 2000. The use of biomarkers to assess the impact of pollution in coastal environments of the Iberian Peninsula: a practical approach. Sci. Total Environ. 247, 295–311.
Craig, R.K., 2012. The Oceans 224–238. doi:10.3390/d4020224
Crowe, T.P., Underwood, A.J., 1999. Differences in dispersal of an intertidal gastropod in two habitats: the need for and design of repeated experimental transplantation. J. Exp. Mar. Bio. Ecol. 237, 31–60.
Dauvin, J.-C., Bellan, G., Bellan-Santini, D., 2010. Benthic indicators: From subjectivity to objectivity - Where is the line? Mar. Pollut. Bull. 60, 947–53.
Dean, H., 2008. The use of polychaetes (Annelida) as indicator species of marine pollution: a review. Rev Biol Trop 56, 11–38.
Díaz-Jaramillo, M., da Rocha, A.M., Chiang, G., Buchwalter, D., Monserrat, J.M., Barra, R., 2013. Biochemical and behavioral responses in the estuarine polychaete Perinereis gualpensis (Nereididae) after in situ exposure to polluted sediments. Ecotoxicol. Environ. Saf. 89, 182–188.
Díaz-Jaramillo, M., Ferreira, J.L., Amado, L.L., Ventura-Lima, J., Martins, a,
25
Retamal, M.R., Urrutia, R., Bertrán, C., Barra, R., Monserrat, J.M., 2010. Biomonitoring of antioxidant and oxidative stress responses in Perinereis gualpensis (Polychaeta: Nereididae) in Chilean estuarine regions under different anthropogenic pressure. Ecotoxicol. Environ. Saf. 73, 515–23.
Díaz-Jaramillo, M., Martins da Rocha, A., Gomes, V., Bianchini, A., Monserrat, J.M., Sáez, K., Barra, R., 2011. Multibiomarker approach at different organization levels in the estuarine Perinereis gualpensis (Polychaeta; Nereididae) under chronic and acute pollution conditions. Sci. Total Environ. 410-411, 126–35.
Díaz-Jaramillo, M., Socowsky, R., Pardo, L.M., Monserrat, J.M., Barra, R., 2013. Biochemical responses and physiological status in the crab Hemigrapsus crenulatus (Crustacea, Varunidae) from high anthropogenically-impacted estuary (Lenga, south-central Chile). Mar. Environ. Res. 83, 73–81.
Douhri, H., Sayah, F., 2009. The use of enzymatic biomarkers in two marine invertebrates Nereis diversicolor and Patella vulgata for the biomonitoring of Tangier’s bay (Morocco). Ecotoxicol. Environ. Saf. 72, 394–9.
Duarte, C.M., Borja, A., Carstensen, J., Elliott, M., Krause-Jensen, D., Marbà, N., 2015. Paradigms in the recovery of estuarine and coastal ecosystems. Estuaries and Coasts 38, 1202–1212.
Elliott, M., Whitfield, A.K., 2011. Challenging paradigms in estuarine ecology and management. Estuar. Coast. Shelf Sci. 94, 306–314.
Faraco, L.F.D., Lana, P. da C., 2003. Response of polychaetes to oil spills in natural and defaunated subtropical mangrove sediments from Paranaguá bay ( SE Brazil ) Hydrobiologia . 321–328.
Ferreira, M., Moradas-Ferreira, P., Reis-Henriques, M.A., 2005. Oxidative stress biomarkers in two resident species, mullet (Mugil cephalus) and flounder (Platichthys flesus), from a polluted site in River Douro Estuary, Portugal. Aquat. Toxicol. 71, 39–48.
Ferreira-Cravo, M., Piedras, F.R., Moraes, T.B., Ferreira, J.L.R., de Freitas, D.P.S., Machado, M.D., Geracitano, L. a, Monserrat, J.M., 2007. Antioxidant responses and reactive oxygen species generation in different body regions of the estuarine polychaeta Laeonereis acuta (Nereididae). Chemosphere 66, 1367–74.
Folk, R., Ward, W., 1957. Brazos River Bar: A Study in the Significance of Grain Size Parameters. J. Sediment. Petrol. Vol. 27, 3–26.
Geracitano, L. a, Bocchetti, R., Monserrat, J.M., Regoli, F., Bianchini, a, 2004. Oxidative stress responses in two populations of Laeonereis acuta (Polychaeta, Nereididae) after acute and chronic exposure to copper. Mar. Environ. Res. 58, 1–17.
Gern, F.R., Lana, P.D.C., 2013. Reciprocal experimental transplantations to assess effects of organic enrichment on the recolonization of benthic macrofauna in a subtropical estuary. Mar. Pollut. Bull. 67, 107–120.
Gomes, T., Gonzalez-Rey, M., Rodríguez-Romero, A., Trombini, C., Riba, I., Blasco, J., Bebianno, M.J., 2013. Biomarkers in Nereis diversicolor
26
(Polychaeta: Nereididae) as management tools for environmental assessment on the southwest Iberian coast. Sci. Mar. 77, 69–78.
Gorbi, S., Baldini, C., Regoli, F., 2005. Seasonal variability of metallothioneins, cytochrome P450, bile metabolites and oxyradical metabolism in the European eel Anguilla anguilla L. (Anguillidae) and striped mullet Mugil cephalus L. (Mugilidae). Arch. Environ. Contam. Toxicol. 49, 62–70.
Gross, M.G., 1971. Procedures in Sedimentary Petrology From inside the book. Wiley Interscience, New York.
Islam, M.S., Tanaka, M., 2004. Impacts of pollution on coastal and marine ecosystems including coastal and marine fisheries and approach for management: A review and synthesis. Mar. Pollut. Bull. 48, 624–649.
Kolm, H.E., Schoenenberger, M.F., Da Rocha Piemonte, M., Souza, P.S.D. a, Schnell E Scühli, G., Mucciatto, M.B., Mazzuco, R., 2002. Spatial variation of bacteria in surface waters of Paranaguá and Antonina Bays, Paraná, Brazil. Brazilian Arch. Biol. Technol. 45, 27–34.
Lana, P.C., Marone, E., Lopes, R.M., Machado, E.C., 2001. Lana, P. C., Marone, E., Lopes, R.M., Machado, E.C. 2000. The subtropical estuarine complex of Paranaguá Bay, Brazil. In Ecological Studies, Coastal Marine Ecosystems of Latin America. Springe.pdf.
Lesser, M.P., 2006. Oxidative stress in marine environments: biochemistry and physiological ecology. Annu. Rev. Physiol. 68, 253–78.
Liu, X., Cheung, S.G., Shin, P.K.S., 2011. Response of meiofaunal and nematode communities to sewage pollution abatement: a field transplantation experiment. Chinese J. Oceanol. Limnol. 29, 1174–1185.
Livingstone, D., 2003. Oxidative stress in aquatic organisms in relation to pollution and aquaculture. Rev. Med. Vet. (Toulouse). 427–430.
López-López, E., Sedeño-Díaz, J.E., Perozzi, F., 2006. Lipid peroxidation and Acetylcholinesterase activity as biomarkers in the BlackSailfui Goodeid, Girardinichthys viviparous (Bustamante) exposed to water from Lake Xochimilco (Mexico). Aquat. Ecosyst. Heal. Manag. 9(3)379-385, 9(3), 1379–385.
Luk’yanova, O.N., 2006. Molecular biomarkers of energy metabolism in mussels under anthropogenic pollution of Peter the Great Bay, the Sea of Japan. Russ. J. Ecol. 37, 205–209.
Machado, A.A. S., Wood, C.M., Bianchini, A., Gillis, P.L., 2014. Responses of biomarkers in wild freshwater mussels chronically exposed to complex contaminant mixtures. Ecotoxicology. 23(7):1345-1358.
Manduzio, H., Rocher, B., Durand, F., Galap, C., Leboulenger, F., 2005. The point about oxidative stress in molluscs. Invertebr. Surviv. J. 2, 91–104.
Maranho, DelValls, T. a., Martín-Díaz, M.L., 2015. Assessing potential risks of wastewater discharges to benthic biota: An integrated approach to biomarker responses in clams (Ruditapes philippinarum) exposed under
27
controlled conditions. Mar. Pollut. Bull. 92, 11–24.
Marone, E., Machado, E.C., Lopes, R.M., Teixeira, E., 2005. Land-ocean fluxes in the paranaguá bay estuarine system, southern brazil. BRAZILIAN J. Oceanogr. 53, 169–181.
Martin, J.P., Bastida, R., 2006. Population structure, growth and production of Laeonereis culveri (Nereididae: Polychaeta) in tidal flats of Río de la Plata estuary, Argentina. J Mar Biol Assoc UK. 86(02):235.
Martinez-Gomez, C., Vethaak, a D., Hylland, K., Burgeot, T., Kohler, a, Lyons, B.P., Thain, J., Gubbins, M.J., Davies, I.M., 2010. A guide to toxicity assessment and monitoring effects at lower levels of biological organization following marine oil spills in European waters. J Mar Sci. 67, 1105-1118.
Martins, C.C., Braun, J. a F., Seyffert, B.H., Machado, E.C., Fillmann, G., 2010. Anthropogenic organic matter inputs indicated by sedimentary fecal steroids in a large South American tropical estuary (Paranaguá estuarine system, Brazil). Mar. Pollut. Bull. 60, 2137–2143.
Martins, C.C., Cabral, A.C., Barbosa-Cintra, S.C.T., Dauner, A.L.L., Souza, F.M., 2014. An integrated evaluation of molecular marker indices and linear alkylbenzenes (LABs) to measure sewage input in a subtropical estuary (Babitonga Bay, Brazil). Environ. Pollut.
Martins, C.C., Ferreira, J.A., Taniguchi, S., Mahiques, M.M., Bícego, M.C., Montone, R.C., 2008. Spatial distribution of sedimentary linear alkylbenzenes and faecal steroids of Santos Bay and adjoining continental shelf , SW Atlantic , Brazil : Origin and fate of sewage contamination in the shallow coastal environment 56, 1359–1363.
Martins, C.C., Venkatesan, M.I., Montone, R.C., 2002. Sterols and linear alkylbenzenes in marine sediments from \nAdmiralty Bay, King George Island, South Shetland Islands. Antarct. Sci. 14, 244–252. doi:10.1017/S0954102002000093
Mizerkowski, B.D., Hesse, K.-J., Ladwig, N., da Costa Machado, E., Rosa, R., Araujo, T., Koch, D., 2012. Sources, loads and dispersion of dissolved inorganic nutrients in Paranaguá Bay. Ocean Dyn. 62, 1409–1424. doi:10.1007/s10236-012-0569-x
Monserrat, J.M., Martínez, P.E., Geracitano, L. a, Amado, L.L., Martins, C.M.G., Pinho, G.L.L., Chaves, I.S., Ferreira-Cravo, M., Ventura-Lima, J., Bianchini, A., 2007. Pollution biomarkers in estuarine animals: critical review and new perspectives. Comp. Biochem. Physiol. C. Toxicol. Pharmacol. 146, 221–34. doi:10.1016/j.cbpc.2006.08.012
Morales, M., 2015. Sciplot: scientic graphing functions for factorial designs. R package version 1.0-6.
Mudge, S.M., Duce, C.E., 2005. Identifying the source, transport path and sinks of sewage derived organic matter. Environ. Pollut. 136, 209–220.
Mudge, S.M., Lintern, D.G., 1999. Comparison of Sterol Biomarkers for Sewage with other Measures in Victoria Harbour, B.C., Canada. Estuar. Coast. Shelf Sci. 48, 27–38.
28
Neto, J.M., Teixeira, H., Patrício, J., Baeta, A., Veríssimo, H., Pinto, R., Marques, J.C., 2010. The Response of Estuarine Macrobenthic Communities to Natural- and Human-Induced Changes: Dynamics and Ecological Quality. Estuaries and Coasts 33, 1327–1339.
Oakes, K.D., Van Der Kraak, G.J., 2003. Utility of the TBARS assay in detecting oxidative stress in white sucker (Catostomus commersoni) populations exposed to pulp mill effluent. Aquat. Toxicol. 63, 447–463.
R Core Team, 2016. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Comput- ing, Vienna, Austria.
Readman, J.W., Fillmann, G., Tolosa, I., Bartocci, J., Mee, L.D., 2005. The use of steroid markers to assess sewage contamination of the Black Sea. Mar. Pollut. Bull. 50, 310–8.
Regoli, F., Gorbi, S., Frenzilli, G., Nigro, M., Corsi, I., Focardi, S., Winston, G.W., 2002. Oxidative stress in ecotoxicology: From the analysis of individual antioxidants to a more integrated approach. Mar. Environ. Res. 54, 419–423.
Reid, D.J., MacFarlane, G.R., 2003. Potential biomarkers of crude oil exposure in the gastropod mollusc, Austrocochlea porcata: laboratory and manipulative field studies. Environ. Pollut. 126, 147–155.
Ribeiro, C.A.D.O., Katsumiti, A., França, P., Maschio, J., Zandoná, E., Cestari, M.M., Vicari, T., Roche, H., de Assis, H.C.S., Filipak Neto, F., 2013. Biomarkers responses in fish (Atherinella brasiliensis) of paranaguá bay, southern Brazil, for assessment of pollutant effects. Brazilian J. Oceanogr. 61, 1–11.
Rocchetta, I., Pasquevich, M.Y., Heras, H., Ríos de Molina, M. del C., Luquet, C.M., 2014. Effects of sewage discharges on lipid and fatty acid composition of the Patagonian bivalve Diplodon chilensis. Mar. Pollut. Bull. 79, 211–219.
Sandrini-Neto, L. & Camargo, M.G., 2014. GAD: an R package for ANOVA designs from general principles. Available on CRAN.
Soorya, S.R., C, A.D., R.N., B., B.V., A., Jayalekshmi, G., Sunny, F., 2013. Quantitative changes in antioxidant enzyme activities , glutathione content and malondialdehyde in a freshwater fish , Anabas testudineus ( bloch ), exposed to sewage. J. Aquat. Biol. Fish. 1(1 & 2), 68–76.
Souza, F.M., Brauko, K.M., Lana, P.C., Muniz, P., Camargo, M.G., 2013. The effect of urban sewage on benthic macrofauna: A multiple spatial scale approach. Mar Pollut Bull. 67(1-2):234-240.
Thain, J.E., Vethaak, A.D., Hylland, K., 2008. Contaminants in marine ecosystems: Developing an integrated indicator framework using biological-effect techniques. ICES J. Mar. Sci. 65, 1508–1514.
Underwood, A.J., 1997. Experiments in Ecology Their Logical Design and Interpretation Using Analysis of Variance. New York: Cambridge University Press.
Venkatesan, M.I., Mirsadeghi, F.H., 1992. Coprostanol as sewage tracer in
29
McMurdo Sound, Antarctica. Mar. Pollut. Bull. 25, 328–333.
Vlahogianni, T., Dassenakis, M., Scoullos, M.J., Valavanidis, A., 2007. Integrated use of biomarkers (superoxide dismutase, catalase and lipid peroxidation) in mussels Mytilus galloprovincialis for assessing heavy metals’ pollution in coastal areas from the Saronikos Gulf of Greece. Mar. Pollut. Bull. 54, 1361–1371.
30
SUPPLEMENTARY MATERIAL
Table 1. ACAP and MDA (nmol/mg) values of acute experiment.. CC = control for the
contaminated area, CUC = control for the uncontaminated area, TPC= transplant from the
uncontaminated to the contaminated area, TPUC= transplant from the contaminated to the
uncontaminated area, TLC= translocation for the contaminated area, TLUC= translocation for the
uncontaminated area.
Uncontaminated to contaminated Contaminated to uncontaminated
Treatment Exposure
time
ACAP (relative
area)
MDA nmol/mg protein
Treatment ACAP
(relative area)
MDA nmol/mg protein
TLC B1
T1 24h
0,471821552 0,019169771
TLUC B1
0,34808171 0,030746714
0,600737191 0,016853222 0,468383632 0,031091731
0,450291231 0,013408843 0,48118622 0,026826847
TPC B1
0,364441781 0,013249381
TPUC B1
0,539330067 0,032016611
0,514550807 0,00859309 0,476685603 0,028754888
0,486099108 0,02211257 0,304097241 0,026623895
CC B1
0,410182864 0,019883001
CUC B1
0,41333602 0,030372703
0,39619807 0,024429118 0,343937735 0,035762519
0,348747004 0,014609157 0,346275105 0,026809451
CUC B1
0,41333602 0,030372703
CC B1
0,410182864 0,019883001
0,343937735 0,035762519 0,39619807 0,024429118
0,346275105 0,026809451 0,348747004 0,014609157
TLC B2
0,540277053 0,011561982
TLUC B2
0,491063773 0,0245045
0,446308173 0,010784968 0,454038189 0,034040329
0,252367067 0,015858759 0,392296267 0,036783076
TPC B2
0,341530709 0,011222763
TPUC B2
0,470033271 0,039322871
0,328684487 0,014391709 0,3931528 0,034449132
0,324292616 0,029966799 0,570886255 0,035501581
CC B2
0,452821412 0,031471541
CUC B2
0,454275005 0,037783338
0,606151874 0,029615983 0,444114907 0,030793102
0,46346649 0,032437011 0,552367534 0,038009484
CUC B2
0,454275005 0,037783338
CC B2
0,452821412 0,031471541
0,444114907 0,030793102 0,606151874 0,029615983
0,552367534 0,038009484 0,46346649 0,032437011
TLC B1
T2 48h
1,091399397 0,055266174
TLUC B1
0,313491061 0,061438804
0,352123539 0,064923774 0,382358536 0,045683956
0,372420613 0,058290154 0,242819623 0,053111987
TPC B1
0,496101209 0,05091721
TPUC B1
0,287075632 0,054564541
0,488952601 0,058365536 0,308398081 0,042900619
0,37630465 0,054457267 0,402722164 0,055399542
CC B1
0,62446319 0,052016048
CUC B1
0,282592273 0,042874525
0,343597319 0,052813358 0,304095554 0,046617534
0,394682076 0,058142289 0,290169265 0,061885298
CUC B1
0,282592273 0,042874525
CC B1
0,62446319 0,052016048
0,304095554 0,046617534 0,343597319 0,052813358
0,290169265 0,061885298 0,394682076 0,058142289
TLC B2
0,283589263 0,060403751
TLUC B2
0,270819961 0,070635414
0,268543827 0,05477909 0,258792666 0,05864387
0,252307286 0,057417462 0,307438838 0,068701574
TPC B2
0,170827691 0,056405603
TPUC B2
0,276593841 0,069681541
0,193001323 0,057239637 0,256092041 0,0614562
0,265719062 0,057115934 0,26213796 0,065973324
CC B2
0,247439369 0,059806493
CUC B2
0,197959775 0,040413011
0,226393252 0,059383194 0,228354186 0,056414301
0,326376563 0,055579299 0,162908624 0,06326247
31
CUC B2
0,197959775 0,040413011
CC B2
0,247439369 0,059806493
0,228354186 0,056414301 0,226393252 0,059383194
0,162908624 0,06326247 0,326376563 0,055579299
TLC B1
T3 96h
0,354250309 0,062157833
TLUC B1
0,3419795 0,064265631
0,41660613 0,050343147 0,373432969 0,058669963
0,461711679 0,070351281 0,275827833 0,027882196
TPC B1
0,206863167 0,064071377
TPUC B1
0,309943318 0,038737211
0,37717497 0,07220394 0,223048176 0,044683694
0,536300973 0,063827835 0,31054963 0,029555097
CC B1
1,156133173 0,067188135
CUC B1
0,241773633 0,04210041
0,629564038 0,072827292 0,398220728 0,052656796
0,714634223 0,08009876 0,250691747 0,03445493
CUC B1
0,241773633 0,04210041
CC B1
1,156133173 0,067188135
0,398220728 0,052656796 0,629564038 0,072827292
0,250691747 0,03445493 0,714634223 0,08009876
TLC B2
0,74206198 0,067191034
TLUC B2
0,243563881 0,039302576
0,370436007 0,064109068 0,189894157 0,026774659
0,310097924 0,051117262 0,300925971 0,031746975
TPC B2
0,456822134 0,072673628
TPUC B2
0,403275997 0,027809713
0,442244888 0,06510933 0,488361544 0,034727465
0,32338532 0,058814929 0,44374833 0,053633863
CC B2
0,433866504 0,06288266
CUC B2
0,373305934 0,056405603
0,303295227 0,063189987 0,345193503 0,041909055
0,330918095 0,037133892 0,732281973 0,043210845
CUC B2
0,373305934 0,056405603
CC B2
0,433866504 0,06288266
0,345193503 0,041909055 0,303295227 0,063189987
0,732281973 0,043210845 0,330918095 0,037133892
Table 2. ACAP and MDA (nmol/mg) values of chronic experiment. CC = control for the
contaminated area, CUC = control for the uncontaminated area, TPC= transplant from the
uncontaminated to the contaminated area, TPUC= transplant from the contaminated to the
uncontaminated area, TLC= translocation for the contaminated area, TLUC= translocation for
the uncontaminated área.
Exposure
time
Uncontaminated to Contaminated Contaminated to uncontaminated