INTRODUCTIONTop

The identification of indicators for the monitoring of aquaculture activities has been on the agenda of the FAO’s General Fisheries Commission for the Mediterranean in recent decades (Massa and Bourdenet 2016Massa F., Bourdenet D. 2016. Sustainable Aquaculture Development in the Mediterranean and the Black Sea. FAO Aquaculture Newsletter 54 March.). However, the need for effective monitoring protocols increased only when the European Water Framework Directive (WFD 2000/60/EC) and the Marine Strategy Framework Directive (MSFD 2008/56/EC) were proposed and Member States were required to achieve a “Good Environmental Status” in their marine waters. It is acknowledged that monitoring protocols for aquaculture, either as a mandatory or voluntary process, are highly inconsistent between countries and regions, ranging from very exhaustive studies to few or no requirements (Read and Fernandes 2003Read P., Fernandes T. 2003. Management of environmental impacts of marine aquaculture in Europe. Aquaculture 226: 139-163., Telfer et al. 2009Telfer T.C., Atkin H., Corner R.A. 2009. Review of environmental impact assessment and monitoring of aquaculture in Europe and North America. In: FAO. Environmental impact assessment and monitoring in aquaculture. FAO Fisheries and Aquaculture Technical. No. 527. Rome, FAO, pp. 285-394.). Thus, the broad variation in monitoring programmes and indicators used has resulted in a range of different approaches and conclusions about the spatial extent and severity of these effects (Kalantzi and Karakassis 2006Kalantzi I., Karakassis I. 2006. Benthic impacts of fish farming: Meta-analysis of community and geochemical data. Mar. Pollut. Bull. 52: 484-493.). In Spain, specific guidelines were developed in order to maintain the ecosystem goods and services provided by the aquaculture activities (FOESA 2011FOESA 2011. Indicadores de sostenibilidad para viveros flotantes en la acuicultura marina Mediterránea. FOESA, Madrid, España. 116 pp.). These guidelines focus on aspects such as the reduction of conversion factors, the compliance codes of good practices in aquaculture and the implementation of Environmental Monitoring Plans (EMPs). Because of the high legislative heterogeneity at regional level, the Spanish Ministry of Agriculture, Food and Environment, through the National Advisory Board of Mariculture (JACUMAR), developed an initiative to unify methodologies, proposing the implementation of site-specific EMPs (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.).

This approach to EMPs focuses on the interactions between aquaculture and benthic ecosystems, in particular, increases in organic enrichment (from uneaten food and fish faeces) and the sensitiveness of benthic assemblages to detecting environmental impacts (Papageorgiou et al. 2010Papageorgiou N., Kalantzi I., Karakassis I. 2010. Effects of fish farming on the biological and geochemical properties of muddy and sandy sediments in the Mediterranean Sea. Mar. Environ. Res. 69: 326-336., Martinez-Garcia et al. 2013Martinez-Garcia E., Sanchez-Jerez P., Aguado-Giménez F., et al. 2013. A meta-analysis approach to the effects of fish farming on soft bottom polychaeta assemblages in temperate regions. Mar. Pollut. Bull. 69: 165-171., Mangion et al. 2014Mangion M., Borg J.A., Thompson R., et al. 2014. Influence of tuna penning activities on soft bottom macrobenthic. Mar. Pollut. Bull. 79: 164-174.). This incremental increase in organic matter (OM) over the benthos may affect sediment biogeochemistry (Karakassis et al. 2005Karakassis I., Pitta P., Krom M.D. 2005. Contribution of fish farming to the nutrient loading of the Mediterranean. Sci. Mar. 69: 313-321.), causing oxygen depletion, augmentation of nutrient efflux and decreased benthic fauna diversity (Pearson and Rosenberg 1978Pearson T.H., Rosenberg R. 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanogr. Mar. Biol. Ann. Rev. 16: 229-311., Hargrave et al. 2008Hargrave B.T., Holmer M., Newcombe C.P. 2008. Towards a classification of organic enrichment in marine sediments based on biogeochemical indicators. Mar. Pollut. Bull. 56: 810-824., Martinez-Garcia et al. 2015Martinez-Garcia E., Sundstein-Carlsson M., Sanchez-Jerez P., et al. 2015. Effect of sediment grain size and bioturbation on decomposition of organic matter from aquaculture. Biogeochemistry 125: 133-148.). When oxygen concentration is reduced, aerobic metabolism is replaced by anaerobic metabolism, sulphate reduction and methanogenesis, producing sulphide and methane, which are harmful for macrobenthic fauna (Holmer et al. 2005Holmer M., Wildish D., Hargrave B. 2005. Organic enrichment from marine finfish aquaculture and effects on sediment processes. In: Hargrave B.T. (ed.), The Handbook of Environmental Chemistry, Water Pollution. Environmental Effects of Marine Finfish Aquaculture, vol. 5. Springer Verlag, pp. 181-206., Hargrave et al. 2008Hargrave B.T., Holmer M., Newcombe C.P. 2008. Towards a classification of organic enrichment in marine sediments based on biogeochemical indicators. Mar. Pollut. Bull. 56: 810-824.). For this reason, a combination of biogeochemical variables and macrofauna are normally selected as bioindicators of aquaculture environmental impacts. The effect on macrofauna has been evaluated by multivariate analyses of the macrofauna assemblages and by using benthic biotic indices, which simplify the complex multivariate benthic assemblages up to a single value to describe the ecological status (Karakassis and Hatziyanni 2000Karakassis I., Hatziyanni E. 2000. Benthic disturbance due to fish farming analyzed under different levels of taxonomic resolution. Mar. Ecol. Prog. Ser. 203: 247-253., Hoey et al. 2010Hoey G.V., Borja A., Birchenough S., et al. 2010. The use of benthic indicators in Europe: From the Water Framework Directive to the Marine Strategy Framework Directive. Mar. Pollut. Bull. 60: 2187-2196., Aguado-Giménez et al. 2015Aguado-Giménez F., Gairín J.I., Martinez-Garcia E., et al. 2015. Application of “taxocene surrogation” and “taxonomic sufficiency” concepts to fish farming environmental monitoring. Comparison of BOPA index versus polychaete assemblage structure. Mar. Environ. Res. 103: 27-35.).

Among all macrobenthic faunal groups, the polychaete assemblages are commonly used for the analysis of disturbances produced by organic enrichment, due to their widespread distribution in the benthos, their trophic flexibility and their quick response to disturbances (Dean 2008Dean K.H. 2008. The use of polychaetes (Annelida) as indicator species of marine pollution: a review. Rev. Biol. Trop. 56: 11-38.). Differences in trophic strategies among species and families can lead to different responses to organic pollution. Some polychaetes are regarded as pollution tolerant, because they can survive in advanced stages of disturbance, while other species are regarded as pollution sensitive, because they are not able to persist under stress conditions (Pearson and Rosenberg 1978Pearson T.H., Rosenberg R. 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanogr. Mar. Biol. Ann. Rev. 16: 229-311., Giangrande et al. 2005Giangrande A., Licciano M., Musco L. 2005. Polychaetes as environmental indicators revisited. Mar. Pollut. Bull. 50: 1153-1162.). Some authors (Tomassetti and Porrello 2005Tomassetti P., Porrello S. 2005. Polychaetes as indicators of marine fish farm organic enrichment. Aquacult. Int. 13: 109-128., Martinez-Garcia et al. 2013Martinez-Garcia E., Sanchez-Jerez P., Aguado-Giménez F., et al. 2013. A meta-analysis approach to the effects of fish farming on soft bottom polychaeta assemblages in temperate regions. Mar. Pollut. Bull. 69: 165-171., Aguado-Giménez et al. 2015Aguado-Giménez F., Gairín J.I., Martinez-Garcia E., et al. 2015. Application of “taxocene surrogation” and “taxonomic sufficiency” concepts to fish farming environmental monitoring. Comparison of BOPA index versus polychaete assemblage structure. Mar. Environ. Res. 103: 27-35.) have tested the use of polychaete assemblages to study disturbances originating from fish farm facilities and concluded that these assemblages give accurate information about the quality of the environment.

The aim of this study was to assess the feasibility of the EMP defined by JACUMAR (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.) for monitoring the aquaculture effects on a Spanish fish farm. The proposed EMP included a specific design with hierarchical sampling along a gradient of distance from the fish cages, using physicochemical variables [sulphides, OM, pH, redox potential (Eh) and δ¹⁵N] and polychaete assemblages as bioindicators, followed by an adjustable monitoring programme. Thus, following the JACUMAR EMP guidelines, the environmental impact of a Mediterranean fish farm dedicated to seabream and seabass farming was evaluated with a particular focus on identifying possible improvements for future EMPs and examining the spatial consistency of the proposed variables that are necessary in order to detect environmental impacts.

MATERIALS AND METHODSTop

Study area

The study was carried out in 2009 in a sea bream and sea bass floating-cage farm at 25-30 m depth in Guardamar bay, southeast Spain (Fig. 1), with a mean annual production of around 1000 t per year. The Segura River flows into Guardamar bay, so the study area was classified as ‘a naturally-high organic concentration at a regional scale’, as described in the JACUMAR protocol (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.).

Sampling strategy

Samples were collected in late summer during the period of warmest water and maximum productivity. Four zones were sampled in the vicinity of the fish farm according to the EMP (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.): zone A (just below the cages); zone B (50 m from the cages, at the edge of the farm facilities defined by the delimitation buoys); and zones C1 and C2 (reference areas placed 1 km away from the fish farm with a similar depth and the same type of sediment). Three stations in each zone were selected randomly, and each had three random replicates collected for sediment analyses and three separate replicates for polychaete assemblage structure. Samples were collected using a Van Veen grab (0.04 m²). Immediately after collection, the samples for the macrobenthos analyses were sieved with seawater through a 1-mm mesh net and the residues were preserved in 10% buffered formalin. At the laboratory, polychaetes were removed and preserved in 70% alcohol, and were later identified to family level. Total free sulphide content was measured with an ion-selective electrode (silver/sulphide combination electrode 9616 BNWP) following the method described by Wildish et al. (1999)Wildish D.J., Akagi H.M., Hamilton N., et al. 1999. A recommended method for monitoring sediments to detect organic enrichment from mariculture in the Bay of Fundy. Can. Tech. Rep. Fish. Aquat. Sci. 2286: 1-34.. Fractions of silt and clay (finest fraction <0.0625 mm) were determined by the wet-sieving method described by Buchanan (1984)Buchanan J.B. 1984. Sediment analysis. In: Holme N.A., McIntyre A.D. (eds) Methods for the study of marine benthos. Blackwell Scientific Publications, Oxford, pp. 41-64.. OM was measured by loss on ignition (400°C, 4 h; Buchanan 1984Buchanan J.B. 1984. Sediment analysis. In: Holme N.A., McIntyre A.D. (eds) Methods for the study of marine benthos. Blackwell Scientific Publications, Oxford, pp. 41-64.). pH and Eh were measured with CRISON electrodes. δ¹⁵N isotopic composition was measured using an EA-IRMS (Thermo Finnigan) analyser in continuous flow configuration, combined with a stable ratio mass spectrometer Deltaplus. The δ¹⁵N isotopic composition is expressed as:

δ¹⁵N(‰) = [(R_sample/R_standard)–1] 10³

where R=¹⁵N/¹⁴N. The standard was atmospheric N₂ while the analytical precision was 0.1‰ (Peterson and Fry 1987Peterson B.J., Fry B. 1987. Stable isotopes in ecosystem studies. Ann. Rev. Ecol. Syst. 18: 293-320.).

Data analyses

Values of environmental variables were classified by the environmental quality standards (EQS) proposed by JACUMAR (Table 1; Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.). The data were analysed according to a two-factor model as suggested in the JACUMAR EMP (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.), using the following model of sources of variability: Xij=µ+Zi+Sj(Zi)+Residual, where Z refers to the zone (fixed and orthogonal with four levels: A, B, C1 and C2) and S refers to the stations (random and nested in Z, with three levels: Station 1, Station 2, Station 3). Analyses of variance (ANOVA) were used to analyse the environmental variables using the above model. Heterogeneity of variance was tested with Cochran’s C test and data were transformed when necessary (Underwood 1997Underwood A.J. 1997. Experiments in Ecology: their logical design and interpretation using analysis of variance. Cambridge University Press, Cambridge, 504 pp.). Where variance remained heterogeneous, untransformed data were analysed and the α-value was set at 0.01, as ANOVA is robust for heterogeneity of variances, particularly for large, balanced experiments (Underwood 1997Underwood A.J. 1997. Experiments in Ecology: their logical design and interpretation using analysis of variance. Cambridge University Press, Cambridge, 504 pp.). Where significant differences were found, data were subsequently investigated using an SNK test (Student-Newman-Keuls) to determine which samples were involved in the differences. The software R (R Development Core Team 2011R Development Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.R-project.org) was used with the GAD package (Sandrini-Neto and Camargo 2014Sandrini-Neto L., Camargo M.G. 2014. GAD: An R package for ANOVA designs from general principles, available on CRAN.) for the ANOVA analyses. To investigate the effects on the polychaete assemblages, the model was analysed using permutational multivariate analysis of variance (PERMANOVA). Pairwise tests and Monte Carlo tests were used to detect differences between levels of the factor Z (Anderson and Robinson 2003Anderson M.J., Robinson J. 2003. Generalized discriminant analysis based on distances. Aust. NZ. J. Stat. 43: 301-318.). Differences in polychaete assemblage structure were explored using non-parametric multidimensional scaling (MDS; Clarke 1993Clarke K.R. 1993. Non-parametric multivariate analysis of changes in community structure. Aust. J. Ecol. 18: 117-143.) and similarity percentages (SIMPER) between zones (Clarke 1993Clarke K.R. 1993. Non-parametric multivariate analysis of changes in community structure. Aust. J. Ecol. 18: 117-143.). To assist with the interpretation of analyses, the variability at each spatial scale was expressed as a component of variation (sum of all pseudo-variance components) (Anderson et al. 2005Anderson M.J., Connell S.D., Gillanders B.M., et al. 2005. Relationships between taxonomic resolution and spatial scales of multivariate variation. J. Anim. Ecol. 74: 636-646.). Multivariate statistical analyses were performed using PRIMER-E software (PRIMER software, Clarke and Gorley 2006Clarke K.R., Gorley R.N. 2006. PRIMER v6: user manual/tutorial. PRIMER-E, Plymouth, 190 pp.) with the PERMANOVA+ package (Anderson et al. 2008Anderson M.J., Gorley R.N., Clarke K.R. 2008. PERMANOVA+ for PRIMER: guide to software and statistical methods.PRIMER-E, Plymouth, 214 pp.).

RESULTSTop

ANOVA analysis of the environmental variables showed that total free sulphide (TFS) was the most sensitive environmental variable to the fish farming activity, showing significant differences for zone factor (Table 2), with zone A having significantly higher values than the other zones (SNK: A>B>C2>C1, Fig. 2). The remaining indicators showed no significant differences between farm and control locations. There was high spatial variability across stations for all indicators except pH, TFS and δ¹⁵N, for which Eh was the most spatially inconsistent (Table 2). The components of variation showed that TFS and Eh had the highest variation among zones, at the scale of hundreds of metres, while the proportion of the finest fraction, pH, OM and δ¹⁵N reflected a higher proportion of variation among replicates at a scale of metres (Table 2).

Despite the significant differences observed in the ANOVA analysis, TFS concentration did not exceed the permitted limits established by the EQS proposed by JACUMAR (Table 1, Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.), with values between 493-1269 μM at zone A and 378-787 μM at zone B (Fig. 2). In zone A, there was no sample above 5000 μM. However, in zone B, the TFS concentration was 267% and 179% higher than in C1 and C2, respectively. The percentage of the finest fraction did not exceed the EQS because values in zones A and B were not 50% and 25% higher than zones C1 and C2, respectively (Fig. 2). OM did not exceed the limit established by the EQS because values in zone A were not 50% higher than in zones C1 and C2. The pH values in zone A were inside the allowable range, but those in zone B were lower than the permitted limits (Fig. 2). This represented the special case considered in the EMP (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.) for areas with a naturally high OM content (due to the outflow of the Segura River), and no significant differences in pH were found between zones C1 and C2 and zone B in pH (Table 2). Eh values in all zones fell outside the allowable range proposed by JACUMAR. Likewise, as it was an area with a naturally high OM content, the EMP proposed a statistical hypothesis test, which showed that zone A was no more electronegative than C1 and C2 (Fig. 2), and zone B did not differ significantly from zones C1 and C2 (Table 2). δ¹⁵N values were very similar across zones and did not exceed the EQS in any samples (Fig. 2).

PERMANOVA analysis of polychaete families showed significant differences among zones in spite of the spatial variability among stations (Table 2). When a pairwise test was applied, only zone A was significantly different to zones B, C1 and C2. The composition of variance showed that the variability occurred at a scale of metres between replicates, and at a zone scale of hundreds of metres (Table 2).

The MDS analysis of the polychaete assemblage structure was in agreement with the PERMANOVA results, as zones C1, C2 and B were tightly clustered, and zone A appeared to be separated in the plot. Moreover, zone A showed higher scatter between its samples (Fig. 3). The SIMPER test indicated that the dissimilarities between zone A and C1 and C2 were 83.87% and 83.82%, respectively. Zone B was less dissimilar than the control zones, with values of 48.95% (C2) and 53.973% (C1), in the latter case slightly exceeding the EQS (Table 3). The number of families in zone A was 64.63% and 70.10% lower than zones C1 and C2, respectively, exceeding the EQS; conversely, the number of families in zone B was 17.07% higher than zone C1 and 1.03% lower than zone C2 (Table 3).

DISCUSSIONTop

The application of EMPs proposed by JACUMAR (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.) led to the detection of an environmental impact due to fish farming, mainly in TFS and polychaete assemblages. Therefore, we conclude that there was a localized environmental impact just underneath the cages, inside the Allowable Zone of Effect. Among all physicochemical variables, only TFS showed significant differences between the impacted and control zones, and only TFS in zone B exceeded the EQS proposed by the JACUMAR approach. Polychaete assemblages at family level were the best indicator of fish farming impacts, as they were sensitive enough to detect an environmental impact in spite of the above-mentioned minor physicochemical effects. However, high spatial variability observed at different scales for several indicators could affect statistical power and increase the probability of a type II statistical error (Underwood 1997Underwood A.J. 1997. Experiments in Ecology: their logical design and interpretation using analysis of variance. Cambridge University Press, Cambridge, 504 pp.). Natural variability between control zones could also negatively affect an environmental impact assessment. From these results we can conclude that although this EMP proposal includes a complementary set of methods and tools that seem to be appropriate for fish farming environmental monitoring, some improvements could be made in order to deal with spatial variability, increased sampling robustness and reducing type II errors, and thus reduce uncertainty in interpreting the results.

The spatial variability for geochemical and biological variables is a normal consequence of benthic assemblage patchiness and irregular disturbance of the seabed by fish farming. This variability can affect the sediment mostly at a scale of metres (Quintino et al. 2006Quintino V., Elliott M., Rodrigues A.M. 2006. The derivation, performance and role of univariate and multivariate indicators of benthic change: Case studies at differing spatial scales. J. Exp. Mar. Biol. Ecol. 330: 368-382., Fernandez-Gonzalez et al. 2013Fernandez-Gonzalez V., Aguado-Giménez F., Gairin J.I., et al. 2013. Exploring patterns of variation in amphipod assemblages at multiple spatial scales: natural variability versus coastal aquaculture effect. Aquacult. Environ. Interact. 3: 93-105.), as found in this study for some geochemical parameters (pH, finest fraction %, OM and δ¹⁵N), and for polychaete assemblages. This significant variability at replicate level confirms the well-known importance of selecting a representative sample size in terms of the volume of sampled sediment and the number of replicates (Andrew and Mapstone 1987Andrew N.L., Mapstone B.D. 1987. Sampling and the description of spatial pattern in marine ecology. Oceanogr. Mar. Biol. 25: 39-90.). Other variables were affected at a scale of hundreds of metres (TFS, Eh and polychaete assemblage), reflecting the interaction of the fish farm cages with the benthic habitat. In some uncertain situations, it may be appropriate to use a higher level of significance (e.g. α=0.1) in order to avoid overlooking slight environmental impacts, thus using a precautionary approach to reduce type II errors, e.g. when the EQS of polychaete assemblages are exceeded but not for physicochemical variables. Replication in higher spatial cases (e.g. nested stations within zones), as performed in this EMP (Underwood 1997Underwood A.J. 1997. Experiments in Ecology: their logical design and interpretation using analysis of variance. Cambridge University Press, Cambridge, 504 pp., Terlizzi et al. 2005Terlizzi A., Benedetti-Cecchi L., Bevilacqua S., et al. 2005. Multivariate and univariate asymmetrical analyses in environmental impact assessment: a case study of Mediterranean subtidal sessile assemblages. Mar. Ecol. Prog. Ser. 289: 27-42.), will increase statistical power and avoid pseudoreplication (Hurlbert 1984Hurlbert S.J. 1984. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54: 187-211.), and would be necessary for detecting organic enrichment from fish farm facilities in a naturally patchy environment.

Dissimilarity tests for polychaete assemblages concluded that values in zones A and B exceeded the EQS proposed by JACUMAR. In this aspect of sensitivity, polychaete assemblages at family level appeared to be a very good tool for identifying changes in the benthic ecosystem, allowing slight differences between zones to be detected even with high spatial heterogeneity. Polychaete assemblages showed higher spatial variability below the fish farm than in other zones. This increased variability has been considered to be a general feature of assemblages in stressed environments and could be due to several scenarios: changes in total cover or total number of taxa, changes in the variance-to-mean ratio for particular species, or changes in taxonomic composition (Warwick and Clarke 1993Warwick R.M., Clarke K.R. 1993. Increased variability as a symptom of stress in marine communities. J. Exp. Mar. Biol. Ecol. 172: 215-226., Chapman et al. 1995Chapman M.G., Underwood A.J., Skilleter G.A. 1995. Variability at different spatial scales between a subtidal assemblage exposed to the discharge of sewage and two control assemblages. J. Exp. Mar. Biol. Ecol. 189: 103-122., Terlizzi et al. 2005Terlizzi A., Benedetti-Cecchi L., Bevilacqua S., et al. 2005. Multivariate and univariate asymmetrical analyses in environmental impact assessment: a case study of Mediterranean subtidal sessile assemblages. Mar. Ecol. Prog. Ser. 289: 27-42.). However, in this case, the dissimilarity results shown in SIMPER, in which zone B was over the limits, are quite misleading regarding the decrease in the number of families, PERMANOVA showed no significant differences between zone B and zones C1 and C2, and MDS clustered all these zones together. In borderline cases such as this, managers may find it difficult to decide whether to apply administrative measures against the farmer. If the SIMPER result was outside the EQS but PERMANOVA and MDS were inside the EQS, should the manager apply any measure? A precautionary approach could be implemented and mitigation measures could be agreed in consensus with the farmer, following a good practice code (FEAP 2006FEAP (Federation of European Aquaculture Producers) 2006. Code of Conduct. Liège, Belgium: Federation of European Aquaculture Producers. www.feap.info). However, observing the natural variability and the dissimilitude result between C1 and C2, a revision of the EQS would be appropriate. We would propose that the limit in zone B (50% of families and 50% of dissimilitude compared with zone C) may be increased to 65% without jeopardizing the achievement of EQS. In the farm studied with this new EQS, the results would have more concordance and only zone A would be outside the EQS.

Analysis of polychaete assemblages could be enhanced with ecological information from univariate analysis of polychaete families. Polychaetes have been well documented as bioindicators, specifically species such as Capitella capitata or some Spionidae species (Pearson and Rosenberg 1978Pearson T.H., Rosenberg R. 1978. Macrobenthic succession in relation to organic enrichment and pollution of the marine environment. Oceanogr. Mar. Biol. Ann. Rev. 16: 229-311., Giangrande et al. 2005Giangrande A., Licciano M., Musco L. 2005. Polychaetes as environmental indicators revisited. Mar. Pollut. Bull. 50: 1153-1162., Tomassetti et al. 2005Tomassetti P., Porrello S. 2005. Polychaetes as indicators of marine fish farm organic enrichment. Aquacult. Int. 13: 109-128.). Moreover, studying the whole assemblage at family level and following general patterns of family abundance, as described by Martinez-Garcia et al. (2013)Martinez-Garcia E., Sanchez-Jerez P., Aguado-Giménez F., et al. 2013. A meta-analysis approach to the effects of fish farming on soft bottom polychaeta assemblages in temperate regions. Mar. Pollut. Bull. 69: 165-171., provides accurate information on the quality of the benthic environment. In this manner, information provided by polychaete assemblages will not be restricted to the number of families and the dissimilarity value, which is a simplified number that summarizes an overall ecological process, as happens with the biotic indices (Aguado-Giménez et al. 2015Aguado-Giménez F., Gairín J.I., Martinez-Garcia E., et al. 2015. Application of “taxocene surrogation” and “taxonomic sufficiency” concepts to fish farming environmental monitoring. Comparison of BOPA index versus polychaete assemblage structure. Mar. Environ. Res. 103: 27-35.). Therefore, univariate analyses of selected polychaete families should also be carried out in order to provide more information on the ecological processes occurring due to fish farming. Considering that classification of polychaetes at family level is required to perform multivariate analyses, this approach would not require additional taxonomic effort.

Sulphide measurements, such as TFS, are able to show variations in biogeochemical processes of the sediment due to organic enrichment (Wildish et al. 1999Wildish D.J., Akagi H.M., Hamilton N., et al. 1999. A recommended method for monitoring sediments to detect organic enrichment from mariculture in the Bay of Fundy. Can. Tech. Rep. Fish. Aquat. Sci. 2286: 1-34.), even when the input is not high enough to be detected by OM measurements (Martinez-Garcia et al. 2015Martinez-Garcia E., Sundstein-Carlsson M., Sanchez-Jerez P., et al. 2015. Effect of sediment grain size and bioturbation on decomposition of organic matter from aquaculture. Biogeochemistry 125: 133-148.). Thus, some authors have proposed TFS as an indicator to be included in EMPs (Katavić et al. 2005Katavić I., Herstad T.J., Kryvi H., et al. 2005. Guidelines to marine aquaculture planning, integration and monitoring in Croatia. Project “Coastal zone management plan for Croatia”, Zagreb, 78 pp., Aguado-Giménez et al. 2015Aguado-Giménez F., Gairín J.I., Martinez-Garcia E., et al. 2015. Application of “taxocene surrogation” and “taxonomic sufficiency” concepts to fish farming environmental monitoring. Comparison of BOPA index versus polychaete assemblage structure. Mar. Environ. Res. 103: 27-35.). In these EMPs, sulphide concentration from zone A and B must be compared with that in control areas by contrasting hypotheses. By this method, in this study, differences produced by the fish farming were detected. Zones A and B were within the permitted limit set by JACUMAR EQS. Again, this could cause a controversial situation for the managers, as zone A clearly suffered statistically significant impacts, and this result was in line with polychaete assemblage results. By contrast, zone B was slightly altered, beyond the limit of 50% higher than controls, but, if these data were related to the polychaete assemblage, the MDS and PERMANOVA showed no important biotic impact caused by the sulphide value in zone B. The EQS limits set by JACUMAR were probably overly high, so other fish farm facilities may be wrongly considered to be within the EQS. We would propose that, coupled with the hypothesis test, an increased percentage threshold with respect to zone C may be added. For areas with naturally-high organic enrichment such as Guardamar bay, the limit for zone A could be significantly different, with double the concentration in zone C. Sulphide concentration limit in zone B could also be revised, taking into account that there was a difference of 50% between C1 and C2. Another suggestion for the limit may be to double the concentration of controls. If these new limits were implemented in the studied fish farm, zone A would clearly exceed the EQS, and zone B would be close to the upper limit but within the EQS compared with C2 and outside the EQS compared with C1.

After many years using Eh to add information on the degree of organic enrichment, some authors do not advocate its use in finfish monitoring. This is due to the problems associated with potential “poisoning” of the probes and the high variability in the Eh measures, and the difficulty of obtaining a stable reading that was also encountered in the present study (Wildish et al. 2005Wildish D.J., Pohle G.W., Hargrave B.T., et al. 2005. Benthic monitoring methods for habitat management of finfish mariculture in Canada. Canadian Science Advisory Secretariat, Research Document 2005/039, 39 pp., MER 2008MER 2008. Evaluation of the variability, applicability, and suitability of the redox potential and total sulfide measurement methodologies (Wildish et al. 1999) for determining benthic organic enrichment state and their use as regulatory compliance standards in Maine. Report prepared for the Maine Aquaculture Innovation Center (MAIC), 28 February 2008, 51 pp.). δ¹⁵N has been used as a good tracer of fish farm waste (Holmer and Frederiksen 2007Holmer M., Frederiksen M.S. 2007. Stimulation of sulfate reduction rates in Mediterranean fish farm sediments inhabited by the seagrass Posidonia oceanica. Biogeochemistry 85: 169-184., Ruiz et al. 2010Ruiz J.M., Marco-Méndez C., Sánchez-Lizaso J.L. 2010. Remote influence of off-shore fish farm waste on Mediterranean seagrass (Posidonia oceanica) meadows. Mar. Environ. Res. 69: 118-126.), but in this study, δ¹⁵N results showed no sign of fish farm effect even below the cages. Considering that it is an expensive variable to measure, we would not propose this variable in EMPs.

A more exhaustive EMP with increased periodicity may be required if surveys reveal significant negative impacts. An adjustable EMP is proposed by JACUMAR for Spain, as has been done by Norway and Croatia (Ervik et al. 1997Ervik A., Hansen P.K., Aure J., et al. 1997. Regulating the local environmental impact of intensive marine fish farming I. The concept of the MOM system (Modelling-Ongrowing fish farms-Monitoring). Aquaculture 158: 85-94., Katavić et al. 2005Katavić I., Herstad T.J., Kryvi H., et al. 2005. Guidelines to marine aquaculture planning, integration and monitoring in Croatia. Project “Coastal zone management plan for Croatia”, Zagreb, 78 pp.). However, in some situations, as shown in the present study, contradictory results could affect the implementation of an adjustable EMP. According to the results of the present study, following an adjustable EMP (Katavić et al. 2005Katavić I., Herstad T.J., Kryvi H., et al. 2005. Guidelines to marine aquaculture planning, integration and monitoring in Croatia. Project “Coastal zone management plan for Croatia”, Zagreb, 78 pp.), it would be necessary to adapt the EMP of the studied fish farm by increasing the sampling periodicity of polychaete assemblages and TFS to a survey every six months, until the values were again within the EQS. If the environmental impact persisted over time, mitigation measures would also be required, such as reducing productivity or relocating facilities to deeper waters.

Consensus about monitoring protocols is needed to ensure that data meet defined standards of quality with a known level of confidence, in order to be credible to external review and allow comparisons between places, regions and agencies. The proposed EMP of JACUMAR seems to be a very innovative approach and a reliable tool for monitoring fish farming along a scale of thousands of kilometres along the entire Spanish coast, and this kind of proposal could be considered for implementation in other European and Mediterranean countries with fish farming under similar environmental conditions. Moreover, this EMP is pioneering in adding a hypothesis test as an EQS for fish farm monitoring, and in proposing comparisons with control locations as natural background values. However, in some cases higher statistical power with regard to univariate and multivariate analysis is needed in order to accurately determine the effects of fish farming.

Therefore, before full implementation of JACUMAR EMP in Spain, it will be necessary to evaluate the need to increase spatial replication at several scales, contrasting the information obtained from various pilot cases. A revision of EQS for environmental variables will be recommended for TFS and polychaete assemblage with the addition of ecological information about their families. The use of Eh and δ¹⁵N would be unwise. Moreover, iterative EMP will be important in order to trace the progression of fish farm activity in relation to benthic environmental quality (Aguado-Giménez et al. 2012Aguado-Giménez F., Carballeira Ocaña A., Collado Sánchez C. et al. 2012. Propuesta Metodológica para la realización de los planes de vigilancia ambiental de los cultivos marinos en jaulas flotantes. Junta Nacional Asesora de Cultivos Marinos (JACUMAR). Ministerio de Agricultura, Alimentación y Medio Ambiente, 164 pp.), and long temp data series from fish farming EMPs carried out with the same methodology will be very informative, giving more accurate information about their environmental status, as required by the European directives (Hoey et al. 2010Hoey G.V., Borja A., Birchenough S., et al. 2010. The use of benthic indicators in Europe: From the Water Framework Directive to the Marine Strategy Framework Directive. Mar. Pollut. Bull. 60: 2187-2196.). Therefore, we encourage future steps for harmonization of EMP at national, European or even Mediterranean scale, such as this EMP proposed by JACUMAR for Spanish coastlines.

ACKNOWLEDGEMENTSTop

We are grateful to the CULMAR fish farm for giving us access and providing assistance during the study. We thank “Spanish National Plans of Aquaculture” (JACUMAR) in particular for the project “Selección de indicadores, determinación de valores de referencia, diseño de programas y protocolos de métodos y medidas para estudios ambientales en acuicultura marina”. We also thank Nick Marchant for his assistance with the English version.

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