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 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 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 1 2 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. 3 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 coletas: Maria Emília Diez, Luiz Silva, Amanda Alvarez, Olalla Alonso, Adriana Sardi, Cristiane de Matos, Rodolfo Rocha, Júlia Porto, Ariane Rodrigues, Flávia de Souza, Christina Lazzarotto, Gustavo de Oliveira, Daniele da Conceição, Madson de Melo, Ilara, Bruna Marcela, Nálita Scamparle, Bárbara Gimenez, Mathilde Lemoine, Estela Pires, Orlando Ricetti, Inara Regina, Aline Cason, Matheus Japur, Jessé Scapini, Bruno Escobar, Fernanda Souza, Eliandro Gilbert, Bianca Possamai, Hectory Siuch, Kalina Brauko, Leonardo Sandrini, Angel Gonzalez, Hugo, Ândrea Lemes, Rodrigo Pelanda, Taynara Pinheiro, Renan Macedo, Julia Castro e Marina Sutili. À minha família e amigos pelo constante apoio e incentivo. Apesar da distância, sempre se fazem presentes nos momentos mais importantes. 4 ABSTRACT This study experimentally evaluated in situ responses of the polychaete Laeonereis culveri to acute or chronic contamination/decontamination by sewage in a subtropical estuary. We assessed levels of the total antioxidant capacity (ACAP) and lipoperoxidation (LPO) through an experiment involving reciprocal transplants between contaminated and uncontaminated intertidal areas in acute/short term (24 to 96 hours) and chronic/medium term (7 to 14 days) time scales. In the acute assay, LPO levels significantly increased in animals transplanted from an uncontaminated to a contaminated area. However, there was no significant decrease in ACAP levels between transplanted worms and those from origin areas. There was no significant decrease of LPO levels in animals transplanted from contaminated to uncontaminated areas, but ACAP levels surprisingly decreased over time. None of the biochemical variables were affected in the chronic assay. Variations in biochemical responses were more related to background variability over time and heterogeneity among areas than to the experimental manipulation itself. Our results strongly suggest that biochemical responses in L. culveri occur on the time scale of hours to days after abrupt contamination or decontamination. Keywords: sewage; oxidative stress; lipoperoxidation; manipulation; recovery 5 RESUMO Avaliamos experimentalmente respostas in situ do poliqueta Laeonereis culveri à contaminação/descontaminação aguda e crônica por esgoto em um estuário subtropical. Analisamos os níveis de capacidade antioxidante contra radicais peroxil (ACAP) e de lipoperoxidação (LPO) com um experimento envolvendo transplantes recíprocos entre áreas contaminadas e não contaminadas em agudo/curto (24 à 96 horas) e crônico/médio (7 à 14 dias) prazo. No experimento agudo, os níveis de LPO aumentaram significativamente nos animais transplantados da área não contaminada para a área contaminada. Contudo, não houve uma diminuição significativa nos níveis de ACAP entre os animais transplantados e os da área de origem. Não houve decréscimo significativo nos níveis de LPO nos animais transplantados da área contaminada para a área não contaminada, mas os níveis de ACAP diminuíram ao longo do tempo. Variações nas respostas bioquímicas no experimento crônico estavam mais relacionadas à variabilidade de fundo ao longo do tempo e à heterogeneidade entre áreas do que à manipulação experimental. Nossos resultados indicam que as respostas bioquímicas em L. culveri ocorrem na escala de horas a dias após contaminação ou descontaminação abruptas. Palavras-chave: esgoto; estresse oxidativo; peroxidação lipídica; manipulação; recuperação 6 TABLE OF CONTENTS 1. INTRODUCTION ................................................................................................................ 8 2.1. Study area ................................................................................................................. 10 2.2. Experimental design and field procedures ........................................................... 11 2.3. Sampling processing and laboratory procedures ................................................ 13 2.3.1. Tissue homogenization .................................................................................... 13 2.3.2. Antioxidant competence against peroxyl radicals determination .............. 13 2.3.3. LPO determination............................................................................................ 14 2.3.4. Coprostanol ....................................................................................................... 14 2.3.5. Grain size and organic matter content .......................................................... 14 2.4. 3. Data analysis ............................................................................................................. 15 RESULTS .......................................................................................................................... 15 3.1. Water and sediment variables ................................................................................ 15 3.2. Acute Experiment ..................................................................................................... 16 3.2.1. Transplant to the contaminated area (UC to C) ............................................... 18 3.2.2. Transplant to the uncontaminated area (C to UC) .......................................... 18 3.3. Chronic experiment .................................................................................................. 18 3.3.1. Transplant to the contaminated area (UC to C) ............................................... 18 3.3.2. Transplant to the uncontaminated area (C to UC) .......................................... 20 4. DISCUSSION .................................................................................................................... 20 5. CONCLUSIONS................................................................................................................ 23 6. REFERENCES ................................................................................................................. 23 SUPPLEMENTARY MATERIAL ............................................................................................ 30 7 1. INTRODUCTION Coastal zones are vulnerable to pollution from multiple sources, including domestic and industrial discharges, agricultural runoff, and ship traffic (Islam and Tanaka, 2004). Sewage discharges, made up by a complex mixture of organic and inorganic components (Craig, 2012), are chronic sources of pollution in developing countries, such as Brazil, with precarious sanitation conditions, and inadequate facilities for the collection, treatment and disposal of domestic wastewater (Martins et al., 2008). There is a need to assess the sensitivity of coastal systems to sewage contamination and their recovery potential from the impact of anthropogenic stresses, as a basis for adequate management. Despite recovery time scales are expected to be dependent on the particular community affected and the magnitude of the impacts (Duarte et al., 2015), estuaries, which have naturally large populations of stress-tolerant strategists, may recover relatively fast (Elliott and Whitfield, 2011). Ongoing monitoring of human activities in coastal areas, particularly in developing countries with basic sanitation problems, is necessary to ensure safer operations and prevent or reduce damage to marine systems (Bevilacqua et al., 2006) Sewage pollution may affect the activity of multiple enzymes and stimulate reactive oxygen species (ROS) production causing significant oxidative damage (Livingstone, 2003; Bebianno et al., 2005; Bianchi et al., 2014), given the capacity to generate reactive oxygen species (ROS), altering the balance of pro-oxidants and antioxidants at the molecular and cellular level (Baussant et al., 2009; Manduzio et al., 2005). Oxidative stress biomarkers are widely used in environmental impact studies since they are considered a key component of the response of organisms exposed to changing environmental conditions and can be used to evaluate exposure to and effect of different contaminants (Cajaraville et al., 2000; Douhri and Sayah, 2009; Gomes et al., 2013; Lesser, 2006; Lushchak, 2011; Maranho et al., 2015; Martinez-Gomez et al., 2010; Monserrat et al., 2007; Thain et al., 2008). For instance, exposure to domestic sewage effluents causes an increase in lipid damage in marine worms (Vlahogianni et al., 2007). Additionally, low capacity to face oxidative stress is known for the polychaetes Laeonereis acuta and Perinereis gualpensis from contaminated coastal areas (Díaz-Jaramillo et al., 2010; Geracitano et al., 2004). Classical oxidative stress studies usually measure a limited number of antioxidants individually, such as SOD, catalase, GPX, and free radical scavengers (Livingstone, 2003). However, determination of total antioxidant capacity is essential to evaluate pollution effects and understand how oxidants interact with the reactive oxygen species generated during oxidative stress (Amado et al., 2009; Regoli et al., 2002). The analysis of total antioxidant 8 capacity may thus provide an overall assessment of the biological responses against a particular type of ROS, such as peroxyl radicals (Bocchetti and Regoli, 2006). Lipid peroxidation (LPO) is the oxidation of cell membranes by ROS which leads to the formation of secondary products (Reid and MacFarlane, 2003). The measurement of lipid peroxidation products represents an effect biomarker besides being considered an index of peroxidation of membrane phospholipids (Gorbi et al., 2005). Lipid peroxidation leads to loss of permeability and integrity of cell membranes. Malondialdehyde (MDA) is an end-product and has been widely used as an indicator of lipid injury caused by ROS (Almeida et al., 2005). Benthic organisms are considered reliable indicators of marine pollution (Burton, 2013; Neto et al., 2010) and evaluation of their responses to disturbance is essential for impact assessments (Dauvin et al., 2010). Nevertheless, the use of oxidative stress responses for field monitoring in estuarine benthic organisms is still scarce in the southern hemisphere (DíazJaramillo et al., 2013). Among macrofauna, polychaetes are often used in environmental studies due to their high species and trophic diversity, sedentary life-style, which ensures chronic exposure to any toxic materials in the environment, resistance to different contamination levels, and abundance in benthic communities (Dean, 2008). Polychaetes belonging to the nereidid genus Laeonereis are among the numerically dominant organisms in Paranaguá Bay, one of the largest and best preserved subtropical estuaries in the South Atlantic (Souza et al., 2013). Laeonereis most probably comprises a complex of cryptic species which occur or co-occur from Florida to Argentina. Species of Laeonereis are considered key species in SW Atlantic estuarine coastal environments for fish, shore birds and carnivore invertebrates. The names Laeonereis culveri (which we favour till a much needed phylogenetic review based on molecular data) and L. acuta are largely interchanged both in the taxonomical and ecological literature in the southwestern Atlantic. Laeonereis, usually under the name Laeonereis acuta, is well known in terms of its antioxidant responses and oxidative damage induced by pollutants, both under experimental and field conditions, in the Patos Lagoon estuary, S Brazil (Geracitano et al. 2002, 2004a,b; Monserrat et al., 2007). In this study we evaluated oxidative stress and antioxidant responses in populations of Laeonereis culveri from a sewage-contaminated subtropical estuary after abrupt recovery or disturbance, by carrying out reciprocal sediment transplants between contaminated and uncontaminated areas at acute and chronic time scales. We carried out two in situ assays to assess the extent and velocity of biochemical responses of L. culveri to abrupt 9 decontamination or contamination to sewage, by evaluating lipid peroxidation, and total antioxidant capacity. We hypothesized that if organisms from contaminated areas show higher MDA levels and a lower capacity to face oxidative stress than those from uncontaminated areas, then their transplant to uncontaminated areas will induce the reduction of MDA levels and the increase of antioxidant competence against peroxyl radicals (ACAP) levels. Conversely, animals transplanted from uncontaminated to contaminated areas will display increased MDA levels and decreased ACAP levels both in short term/acute (24, 48 and 96 hours) and medium term/chronic (7 to 14 days) time scales. Responses of Laeonereis culveri to sewage disturbance and subsequent ecological recovery may provide a better tool to assess the putative effect of these complex mixture of contaminants and to develop management policies. 2. MATERIALS AND METHODS 2.1. Study area The Paranaguá Bay (25º30’ S, 48º25’ W), located in the coastal plain of Paraná State, in Southern Brazil, is a semi-closed estuarine system, bordered by extensive tidal plains and colonized by mangroves and marshes (Fig.1). The bay has an average depth of 5.4 m, with a surface area of about 250 km² (Lana et al., 2001). Estuarine hydrodynamics is mainly regulated by tidal currents, and markedly seasonal fresh-water input (Marone et al., 2005). Macrobenthic communities of local mangroves are numerically dominated by crabs and polychaetes (Faraco and Lana, 2003). Some sectors of the Paranaguá Bay are impacted by urban, industrial, agricultural and harbour activities (Ribeiro et al., 2013). The lack of a sanitation system is still a basic health problem. Levels of inorganic nutrients from Paranagua city sewage exceed threshold limits determined by the Brazilian environmental legislation (Mizerkowski et al., 2012). Most of the sewage from Paranaguá city is discharged into Itiberê and Emboguaçú rivers (Kolm et al., 2002), creating dispersion plumes and sedimentary environments with high levels of organic matter, faecal coliforms and faecal steroid concentrations in the vicinity of Paranaguá city (Abreu-Mota et al., 2014; Martins et al., 2010). We selected two experimental areas in tidal plains at the mouth of Chumbo river and at Cotinga Island, which are about 3 km apart from each other, and display different levels of human pressure (Fig. 1). Both were previously prospected for dense populational patches of Laeonereis. We selected the tidal plain at the mouth of Chumbo river as an area contaminated by sewage. Sewage is discharged directly into the estuary about 300 m from the experimental area, without even primary treatment. Sewage discharge creates 10 a sharp and compressed contamination gradient which is limited to areas near the city of Paranaguá (Barboza et al., 2015; Brauko et al., 2015). In contrast, the tidal plain of Cotinga Island is uncontaminated by sewage, as indicated by the concentrations of faecal sterols (see section of results). Figure 1. Study areas in the tidal plains near the Chumbo River (Contaminated – C) and Cotinga Island (Uncontaminated – UC). Modified from Gern and Lana (2013). 2.2. Experimental design and field procedures We carried out two in situ experiments to evaluate the response of oxidative stress biomarkers of Laeonereis culveri after acute/short term (April 2015) and chronic/medium term (May 2015) exposure to sewage. Both field experiments were licensed by SISBIO (license no. 36255, of the Brazilian Ministry of Environment). In each field assay, we transplanted worms between the contaminated and the uncontaminated area to simulate either abrupt contamination or decontamination by sewage (transplant treatment). To assess the effects of experimental artifacts (i.e, to discriminate between the effect of the new environmental conditions and the effect of experimental handling), as suggested by Crowe and Underwood (1999), worms were also translocated within each of the origin areas (translocation treatment). Control treatments to assess background variation included worms from each area, without any manipulation. 11 Two blocks were established 40 m apart from each other, in each area. Each block included three experimental sites, 4 m apart from each other, corresponding to the treatments (transplantation, translocation, and control) (Fig. 2). Test organisms were transported in sediment blocks within an essay chamber (Fig. 2), modified from Burton et al. (2005) and originally designed for in situ assays with Hediste diversicolor, to reduce transportation stress. Each chamber consisted of 10 cm inner diameter plastic pots with openings in the top and two rectangular windows. To prevent the escape of the test organism and allow the proper exchange of interstitial and overlying waters, the openings were covered with a 40 µm nylon mesh. We have taken three replicates for each treatment for each site over three successive sampling times (24, 48, and 96 hours) for the acute experiment and two successive sampling times (7 and 14 days) in the chronic experiment. All samples were performed simultaneously in both tidal flats at same time. Surface sediment samples were collected in each experimental site to assess grain size, organic matter content and contamination levels using coprostanol as a geochemical marker. Figure 2. The schematic arrangement of the sampling sites in the contaminated (C) and uncontaminated area (UC) and the essay chambers. B1 = block 1, B2 = block 2, Cuc = control 12 for the uncontaminated area, Cc = control for the contaminated area, TL = translocate, TP = transplant. 2.3. Sampling processing and laboratory procedures Samples were screened through a 0.5 mm mesh. A pool of 6 to 9 polychaetes weighing between 35 and 55 mg were collected from each replicate, cleaned and kept frozen at −80 °C until biochemical analysis. Only complete, undamaged individuals were selected. 2.3.1. Tissue homogenization For the biochemical measurements, organisms were homogenized (1:3 w:v) in buffer solution containing 0.5 M sucrose, 20 mMTris-base, 1 mM EDTA, 1 mMdithiothreitol (DTT) and 0.15 M KCL with pH adjusted to 7.6 and adding protease inhibitor (0.1 mMphenylmethylsulfonyl fluoride, PMSF) (Geracitano et al., 2004). Homogenates were centrifuged at 10.000g for 20 min at -4ºC. Supernatants were collected, stored at -80 ºC and employed later to determine total protein content, antioxidant capacity against peroxyl radical, and LPO levels. Protein concentration was determined using a commercial reagent kit ('ProteínasTotais', Doles Reagentes, Lagoa Santa, MG, Brazil), which is based on the Biuret method. Absorbance readings (550 nm) were performed in triplicate using a microplate reader (ELx808IU, BioTek Instruments, Winooski, VT, USA). 2.3.2. Antioxidant competence against peroxyl radicals determination Antioxidant competence against peroxyl radicals (ACAP) was measured according to Amado et al. (2009). Briefly, homogenate supernatant obtained for ACAP measurements had its protein content adjusted to 1.5 mg/ml. In a white 96-well microplate, we pipetted 10 µL of the supernatant, 127.5 µL of reaction buffer (30 mM HEPES, 200 mMKCl, and 1 mM MgCl2,) with pH adjusted to 7.2, and prepared a half of the wells with 7.5 µL ultrapure water and the other half (with the same samples) 7.5 µL of 4 mM ABAP (2,2′-azobis 2 methylpropionamidinedihydrochloride, a ROS producer). Then, we put the microplate into a fluorescence microplate reader (Victor 2, Perkin Elmer) and added 10 µL of H2DCF-DA (2′,7′ dichlorofluorescein diacetate), a fluorescent probe. The readings were taken every 5 min during 30 min at 37ºC. At this temperature, thermal decomposition of ABAP produces ROS and the nonfluorescent compound H2DCF is oxidized by ROS to the fluorescent compound DCF, allowing to detect competence in neutralizing peroxyl radicals. Therefore, we can measure the antioxidant capacity with the relative difference between ROS area with and without ABAP and the data were expressed as 1/relative area, where a smaller area means a lower antioxidant capacity. 13 2.3.3. LPO determination Lipid peroxidation was measured by thiobarbituric acid reactive substances (TBARS) determination as described by Oakes & Van Der Kraak (2003). Samples homogenates obtained for LPO determination had its protein content adjusted to 3 mg/ml. We added 20 µL of tissue homogenate in test tubes with the reaction mixture containing 67 mM BHT, 20 % acetic acid, 0,8% thiobarbituric acid (TBA), ultrapure water and 8,1% sodium dodecyl sulfate (SDS). Then, tubes were heated in a water bath at 95 °C for 30min. After cooling, ultrapure water and n-butanol were added with thorough vortexing. After centrifugation at 825 xg for 10 minutes at 15 °C, 150 µL of the supernatant were put in a 96-well clear polystyrene plate and its fluorescence measured on a fluorescence microplate reader (excitation: 515 nm; emission: 553 nm; Victor 2, Perkin Elmer). Under these conditions of high temperature and acidity, malondialdehyde (MDA), an end product of lipid peroxidation, reacts with thiobarbituric acid (TBA), allowing its measurement. Concentration of TBARS was calculated employing a standard curve built with tetramethoxypropane (TMP) and the results were expressed as nmol MDA/ mg of protein. 2.3.4. Coprostanol The anthropogenic input of sedimentary organic matter, represented by sewage contribution, was evaluated by fecal sterol concentrations, such as coprostanol and epicoprostanol. Coprostanol is produced in the digestive tracts of humans by microbial reduction of cholesterol (Venkatesan and Mirsadeghi, 1992) and epicoprostanol is formed during wastewater treatment and sewage sludge digestion (Mudge and Lintern, 1999). These sterols are widely used as tracers for human waste along coastal areas (Readman et al., 2005). The top 2 cm of surface sediment was collected with a spoon and placed in pre-cleaned aluminum foil and stored at –20 ºC until determination. The identification and quantification of sterols occurred by 2 µL of the resulting final extract injection into a gas chromatograph (Agilent 7890A GC) coupled with a flame ionization detector (GC-FID). 2.3.5. Grain size and organic matter content Particle size analysis was conducted using the laser diffraction method measured by the MICROTRAC Bluewave laser diffraction particle size analyzer. Sediment parameters were estimated according to Folk and Ward, (1957) method. The CO3 content and organic matter was calculated as the difference between the initial and final weights of each sample after chemical attack, using a solution of hydrochloric acid and hydrogen peroxide (Gross, 1971). 14 2.4. Data analysis The statistical design demands that experiments involving animal transplantation from uncontaminated to contaminated areas and from contaminated to uncontaminated areas be analyzed separately. Biomarker responses were individually tested using an analysis of variance (ANOVA). The linear model employed to test the hypothesis consisted of three factors: Treatments (fixed, four levels: transplantation, translocation, contaminated control, and uncontaminated control), Time (acute experiment: fixed, three levels: 24, 48, and 96 hours/ fixed; chronic experiment: two levels: 7 and 14 days), and Blocks (random, two levels: block 1 and block 2). The homogeneity of variances was assessed by the Cochran’s C test. In the absence of homogeneity, data were log-transformed prior to analysis of variance (Underwood, 1997). For significant terms (P < 0.05), a posteriori test was performed using Student–Newman–Keuls (SNK) test. All statistical analyses and graphs were done into the R environment (R Core Team, 2016) combined with GAD (Sandrini-Neto, L. & Camargo, 2014) and sciplot (Morales, 2015) packages. 3. RESULTS 3.1. Water and sediment variables Water regime varied from mesohaline to polyhaline (Tables 1 and 2). Average temperatures were lower during the chronic than than during the acute experiment. Sediment texture was similar between experimental areas, with average grain diameter corresponding to fine sand. Organic matter content was similar at all experimental blocks, varying from 1,69 to 2,2% (Table 3). Table 1. Values of environmental parameters in experimental areas during the acute experiment. Uncontaminated area Contaminated area 24h 48h 96h Salinity (%) 30 25 20 pH 8 7.9 7.9 Temperature (°C) 26.2 30.4 28.6 Dissolved oxygen (mg/L) 9.2 11.4 9.5 Salinity (%) 20 15 15 pH 7.7 7.9 7.5 Temperature (°C) 25.2 27 27.7 Dissolved oxygen (mg/L) 6.8 5 6.3 Table 2. Values of environmental parameters in experimental areas during the chronic experiment. T0 = field deployment. 15 T0 Salinity (%) 7 days 25 14 days 20 pH 7.8 Temperature (°C) 22.8 Dissolved oxygen (mg/L) 5.0 Salinity (%) 20 25 25 pH 7.7 7.4 7.7 Temperature (°C) 23 23.3 22.1 Dissolved oxygen (mg/L) 4.0 4.8 4.9 Uncontaminated area Contaminated area Nd 8.1 22.9 6.9 Nd= Not determined Total sterol concentrations varied between 1.63 and 9.66 µg g -1, with large variations between the sites of the contaminated area. Coprostanol concentrations varied between 0.01 and 0.44 µg g–1. The highest concentration of coprostanol (0.44 µg.g-1) was found at B1 of the contaminated area, which was about 300 m from the sewage outfall. At the uncontaminated area, the concentration of coprostanol was comparatively low and and epicoprostanol was not detected (Table 3). Table 3. Sediment parameters, sterols concentrations (in µg.g-1) and sterol diagnostic indices in surface sediments from experimental blocks. nd = not detected. Contaminated Uncontaminated area area B1 B2 B1 B2 Particle size (µm) 172.70 205.90 225.6 213.4 250 - 125: fine sand CO3 content 1.73 3.33 1.61 1.92 Organic matter (%) 1.69 1.32 1.84 2.20 Sterols Coprostanol Epicoprostanol Fecal sterol (cop+epic) Total sterol 0.44 0.09 0.53 9.66 0.09 0.01 0.10 1.63 0.03 Nd 0.03 5.42 0.01 Nd 0.01 1.80 Sterol diagnostic indices % of faecal sterols/total sterols epicoprostanol/coprosta nol Threshold levels 5.49 6.13 0.55 0.56 0.20 0.11 Nd Nd >50%: high sewage contamination <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 16 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. 17 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 18 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). 19 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 20 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 21 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 long22 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 shortterm 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. 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Coprostanol as sewage tracer in 28 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. 29 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 Treatment Exposure time TLC B1 TPC B1 CC B1 CUC B1 T1 24h TLC B2 TPC B2 CC B2 CUC B2 TLC B1 TPC B1 CC B1 CUC B1 TLC B2 TPC B2 CC B2 T2 48h ACAP (relative area) MDA nmol/mg protein 0,471821552 0,600737191 0,450291231 0,364441781 0,514550807 0,486099108 0,410182864 0,39619807 0,348747004 0,41333602 0,343937735 0,346275105 0,540277053 0,446308173 0,252367067 0,341530709 0,328684487 0,324292616 0,452821412 0,606151874 0,46346649 0,454275005 0,444114907 0,552367534 1,091399397 0,352123539 0,372420613 0,496101209 0,488952601 0,37630465 0,62446319 0,343597319 0,394682076 0,282592273 0,304095554 0,290169265 0,283589263 0,268543827 0,252307286 0,170827691 0,193001323 0,265719062 0,247439369 0,226393252 0,326376563 0,019169771 0,016853222 0,013408843 0,013249381 0,00859309 0,02211257 0,019883001 0,024429118 0,014609157 0,030372703 0,035762519 0,026809451 0,011561982 0,010784968 0,015858759 0,011222763 0,014391709 0,029966799 0,031471541 0,029615983 0,032437011 0,037783338 0,030793102 0,038009484 0,055266174 0,064923774 0,058290154 0,05091721 0,058365536 0,054457267 0,052016048 0,052813358 0,058142289 0,042874525 0,046617534 0,061885298 0,060403751 0,05477909 0,057417462 0,056405603 0,057239637 0,057115934 0,059806493 0,059383194 0,055579299 Contaminated to uncontaminated Treatment TLUC B1 TPUC B1 CUC B1 CC B1 TLUC B2 TPUC B2 CUC B2 CC B2 TLUC B1 TPUC B1 CUC B1 CC B1 TLUC B2 TPUC B2 CUC B2 ACAP (relative area) MDA nmol/mg protein 0,34808171 0,468383632 0,48118622 0,539330067 0,476685603 0,304097241 0,41333602 0,343937735 0,346275105 0,410182864 0,39619807 0,348747004 0,491063773 0,454038189 0,392296267 0,470033271 0,3931528 0,570886255 0,454275005 0,444114907 0,552367534 0,452821412 0,606151874 0,46346649 0,313491061 0,382358536 0,242819623 0,287075632 0,308398081 0,402722164 0,282592273 0,304095554 0,290169265 0,62446319 0,343597319 0,394682076 0,270819961 0,258792666 0,307438838 0,276593841 0,256092041 0,26213796 0,197959775 0,228354186 0,162908624 0,030746714 0,031091731 0,026826847 0,032016611 0,028754888 0,026623895 0,030372703 0,035762519 0,026809451 0,019883001 0,024429118 0,014609157 0,0245045 0,034040329 0,036783076 0,039322871 0,034449132 0,035501581 0,037783338 0,030793102 0,038009484 0,031471541 0,029615983 0,032437011 0,061438804 0,045683956 0,053111987 0,054564541 0,042900619 0,055399542 0,042874525 0,046617534 0,061885298 0,052016048 0,052813358 0,058142289 0,070635414 0,05864387 0,068701574 0,069681541 0,0614562 0,065973324 0,040413011 0,056414301 0,06326247 30 CUC B2 TLC B1 TPC B1 CC B1 CUC B1 T3 96h TLC B2 TPC B2 CC B2 CUC B2 0,197959775 0,228354186 0,162908624 0,354250309 0,41660613 0,461711679 0,206863167 0,37717497 0,536300973 1,156133173 0,629564038 0,714634223 0,241773633 0,398220728 0,250691747 0,74206198 0,370436007 0,310097924 0,456822134 0,442244888 0,32338532 0,433866504 0,303295227 0,330918095 0,373305934 0,345193503 0,732281973 0,040413011 0,056414301 0,06326247 0,062157833 0,050343147 0,070351281 0,064071377 0,07220394 0,063827835 0,067188135 0,072827292 0,08009876 0,04210041 0,052656796 0,03445493 0,067191034 0,064109068 0,051117262 0,072673628 0,06510933 0,058814929 0,06288266 0,063189987 0,037133892 0,056405603 0,041909055 0,043210845 CC B2 TLUC B1 TPUC B1 CUC B1 CC B1 TLUC B2 TPUC B2 CUC B2 CC B2 0,247439369 0,226393252 0,326376563 0,3419795 0,373432969 0,275827833 0,309943318 0,223048176 0,31054963 0,241773633 0,398220728 0,250691747 1,156133173 0,629564038 0,714634223 0,243563881 0,189894157 0,300925971 0,403275997 0,488361544 0,44374833 0,373305934 0,345193503 0,732281973 0,433866504 0,303295227 0,330918095 0,059806493 0,059383194 0,055579299 0,064265631 0,058669963 0,027882196 0,038737211 0,044683694 0,029555097 0,04210041 0,052656796 0,03445493 0,067188135 0,072827292 0,08009876 0,039302576 0,026774659 0,031746975 0,027809713 0,034727465 0,053633863 0,056405603 0,041909055 0,043210845 0,06288266 0,063189987 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. Uncontaminated to Contaminated Contaminated to uncontaminated Exposure time Treatment ACAP (relative area) TLC B1 TPC B1 CC B1 T1 7 dias CUC B1 TLC B2 MDA nmol/mg protein Treatment ACAP (relative area) MDA nmol/mg protein 0,711665 0,04099 TLUC B1 0,464844 0,034966 0,372835 0,044289 0,377924 0,049556 0,485432 0,047726 0,302121 0,040589 0,535231 0,035103 0,228689 0,053153 0,384314 0,040472 0,349121 0,03011 0,272635 0,052835 0,25658 0,030265 TPUC B1 0,213492 0,0386 0,901127 0,029326 0,20291 0,064324 CUC B1 0,776922 0,033057 0,150191 0,040763 0,684896 0,021145 0,901127 0,029326 0,776922 0,033057 0,684896 0,021145 0,347999 0,046288 0,477098 0,448704 CC B1 0,213492 0,0386 0,20291 0,064324 0,150191 0,040763 0,688432 0,028599 0,039918 0,829736 0,042641 0,04181 0,387617 0,041217 TLUC B2 31 TPC B2 CC B2 CUC B2 TLC B1 TPC B1 CC B1 CUC B1 T2 14 dias TLC B2 TPC B2 CC B2 CUC B2 0,342197 0,03957 TPUC B2 0,556107 0,063517 0,253194 0,04344 0,627461 0,056781 0,639318 0,043504 1,065994 0,057768 0,590208 0,062502 2,052966 0,057651 0,498469 0,051355 0,528317 0,079784 0,466547 0,03163 0,275713 0,07308 2,052966 0,057651 0,590208 0,062502 0,528317 0,079784 0,498469 0,051355 0,275713 0,07308 0,466547 0,03163 0,61003 0,060879 0,387909 0,09353 0,681789 0,063365 0,397642 0,083222 0,718408 0,064743 0,66573 0,097955 0,252265 0,092813 0,491727 0,126597 CUC B2 CC B2 TLUC B1 TPUC B1 0,25338 0,109595 0,616704 0,109574 0,342104 0,06323 0,445622 0,077278 0,26748 0,091045 1,895408 0,061643 0,319505 0,066926 0,339472 0,080438 0,237035 0,050002 0,243595 0,0795 1,895408 0,061643 0,26748 0,091045 0,339472 0,080438 0,319505 0,066926 0,237035 0,050002 0,290841 0,316921 0,208146 0,280574 0,206827 0,307914 0,104591 0,164734 0,147973 0,331891 0,269849 0,131747 0,071401 0,077828 0,146642 0,081983 0,089191 0,052144 0,102805 0,071074 0,11963 0,057921 0,058798 0,081127 0,243595 0,0795 0,466821 0,277348 0,390607 0,525852 0,59869 0,369857 0,331891 0,269849 0,131747 0,104591 0,164734 0,147973 0,063173 0,062903 0,047765 0,090817 0,081201 0,078815 0,057921 0,058798 0,081127 0,102805 0,071074 0,11963 CUC B1 CC B1 TLUC B2 TPUC B2 CUC B2 CC B2 Table 3. Total sterol concentrations (in µg.g-1) and sterol diagnostic indices in surface sediments from experimental blocks. nd = not detected. Contaminated Uncontaminated area area B1 B2 B1 B2 Sterols Coprostanol 0,44 0,09 0,03 0,01 Epicoprostanol 0,09 0,01 nd nd Colesterol 2,55 0,64 2,43 0,72 Cholestanol 0,69 0,09 0,59 0,09 Campesterol 1,51 0,10 1,06 0,11 Stigmasterol 0,87 0,34 0,28 0,50 Sitosterol 2,65 0,26 0,81 0,27 Sitostanol 0,48 0,05 0,14 0,06 Dinosterol 0,38 0,05 0,08 0,04 Fecal sterol (cop+epic) 0,53 0,10 0,03 0,01 Total sterol 9,66 1,63 5,42 1,80 Sterol diagnostic indices I Threshold levels coprostanol/(coprostanol + 0,39 0,50 0,05 0,10 <0.30: pristine 32 cholestanol) coprostanol/(coprostanol + cholesterol) coprostanol/(coprostanol + dinosterol) 0,15 0,12 0,01 0,01 0,54 0,64 0,27 0,20 % of faecal sterols/total sterols 5,49 6,13 0,55 0,56 epicoprostanol/coprostanol 0,20 0,11 nc nc environments >0.50: sewage contamination >0.50: sewage contamination >50%: high sewage contamination <0.20: untreated sewage input Table 4. Acute experiment. Analysis of variance of lipid peroxidation levels and antioxidant capacity against peroxyl on transplantations to the contaminated (UC to C) and uncontaminated areas (C to UC). Significant terms of interest (α = 0.05) used in a posteriori comparisons are highlighted in bold. For Student–Newman–Keuls (SNK) tests the mean values are listed in ascending order. ‘‘>’’ indicates p < 0.05 and ‘‘=’’ indicates p > 0.05. 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. UC to C C to UC ACAP Source of variation df MS F Pr(>F) MS F Pr(>F) Treatment (Tr) 3 0.45475 35.333 0.16374 0.046893 1.8689 0.3101767 Time (T) 2 0.43329 0.5336 0.122191 2.6329 0.2752616 Block (B) 1 131.365 0.65208 24.6437 9.105e-06 0.012558 2.7228 0.1054568 Tr x T 6 0.12966 0.7703 0.015662 0.6673 Tr x B 3 0.12870 2.4144 0.025091 5.4402 0.6821548 0.0026576 TxB 2 0.81207 0.046409 10.0622 0.0002241 Tr x T x B 6 0.16832 0.023471 5.0888 0.0004114 Residuals 48 0.05331 SNK test 0.62026 0.07803 15.2342 7.535e-06 3.1576 0.01084 0.004612 T1.B1 Cuc=Cc=TPc=TLc T1.B1 Cuc=Cc=TLuc=Tpuc T1.B2 TPc=TLc=Cuc=Cc T1.B2 T2.B1 Cuc=Cc=TPc<TLc T2.B1 TLuc=Tpuc=Cuc=Cc Cuc=TLuc=Tpuc<Cc T2.B2 Cuc=TPc=Cc=TLc T2.B2 Cuc=TPuc=Cc=TLuc T3.B1 Cuc=TPc=TLc<Cc T3.B1 T3.B2 Cuc=Cc=TPc=TLc T3.B2 TPuc=Cuc=TLuc<Cc TLuc=Cuc=Cc=TPc MDA Source of variation df MS F Pr(>F) MS F Pr(>F) 0.21810 0.01061 0.01830 0.5989 0.658010 242.329 10.1085 0.090021 0.10057 2.6838 0.107914 3 0.0004713 4.8875 0.71698 0.03737 0.22133 3.6592 0.069772 Tr x B 3 0.0000321 0.6480 0.58808 0.03056 0.8156 TxB 1 0.0001049 2.1197 0.13117 0.23973 6.3974 0.491611 0.003444 Tr x T x B 3 0.0000964 1.9495 0.09182 0.06049 1.6141 0.163751 Residuals 32 0.0000495 Treatment (Tr) 3 0.0000866 2.7006 Time (T) 1 0.0097817 93.2917 Block (B) 1 0.0000066 0.1330 Tr x T SNK test T1 TLc=TPc=Cc=Cuc B1 T1<T3=T2 33 T2 Cuc=TPc=Cc=TLc T3 Cuc<TLc=Cc=TPc B2 T1<T3<T2 Table 5. Chronic experiment. Analysis of variance of lipid peroxidation levels and antioxidant capacity against peroxyl on transplant to the contaminated area (UC to C) and uncontaminated area (C to UC). Significant terms of interest (α = 0.05) used in a posteriori comparisons are highlighted in bold. For Student–Newman–Keuls (SNK) tests, the mean values are listed in ascending order. ‘‘>’’ indicates p < 0.05 and ‘‘=’’ indicates p > 0.05. 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. UC to C C to UC ACAP Source df MS F Pr(>F) MS F Pr(>F) Treatment (Tr) 3 0.084206 0.6903 0.6160 0.077663 0.5534 0.680454 Time (T) 1 0.036069 6.5565 0.2370 0.081397 0.9696 0.504911 Block (B) 1 0.020537 1.8884 0.1789 0.037703 2.3313 0.136617 Tr x T 3 0.025572 0.2162 0.8798 0.018755 0.1141 0.946125 Tr x B 3 0.121992 11.2177 3.446e-05 0.140327 8.6771 0.000234 TxB 1 0.005501 0.5059 0.4821 0.083948 5.1909 0.029523 Tr x T x B 3 0.118291 10.8774 4.399e-05 0.164431 10.1675 7.406e-05 Residuals 32 0.010875 SNK test T1.B1 Cc<TPc=TLc<Cuc T1.B1 Cc=TPuc=TLuc<Cuc T1.B2 Cuc=TPc=TLc=Cc T1.B2 Cuc<Cc=TLuc=Tpuc T2.B1 Cc=TPc=Cuc<TLc T2.B1 Cc=Cuc=TLuc=Tpuc T2.B2 Cc=TLc=Cuc=TPc T2.B2 TLuc=Cc=Cuc=Tpuc MDA Source df MS F Pr(>F) MS F Pr(>F) Treatment (Tr) Time (T) Block (B) Tr x T Tr x B TxB Tr x T x B Residuals SNK test 3 1 1 3 3 1 3 32 0.0005656 0.0102389 0.0004925 0.0005104 0.0009559 0.0001689 0.0000393 0.0000552 T1 T2 0.5917 60.6052 2.9022 12.9730 5.6325 0.9955 0.2318 0.661472 0.081330 0.098154 0.031795 0.003236 0.325886 0.873536 0.0350 46.164 0.2783 0.1320 0.2732 0.3661 0.1665 0.1280 12.6097 4.4392 0.7924 4.3593 5.8405 2.6570 0.93737 0.17475 0.04305 0.57356 0.01105 0.02154 0.06506 TPc=TLc=Cc=Cuc TLc=Cc<Cuc=TPc B1 B2 Cuc=Cc=TPuc=Tluc Cc=TLuc=TPuc=Cuc B1 B2 Cuc=TLc=Cc=TPc TLc=Cc=TPc<Cuc B1 B2 T1<T2 T1<T2 34