Broad-scale effects of marine salmonid aquaculture on ... - ecasa

Broad-scale effects of marine salmonid aquaculture on ... - ecasa

Journal ong>ofong> Experimental Marine Biology and Ecology 327 (2005) 70–>Broadong>-ong>scaleong> ong>effectsong> ong>ofong> ong>marineong> ong>salmonidong> ong>aquacultureong> onmacrobenthos and the sediment environmentin southeastern TasmaniaGraham J. Edgar a,b, *, Catriona K. Macleod a , Ron B. Mawbey b , Derek Shields ba Tasmanian Aquaculture and Fisheries Institute, University ong>ofong> Tasmania, Marine Research Laboratory, Taroona, Tasmania 7053, Australiab Aquenal Pty Ltd, GPO Box 828, Hobart, Tasmania 7001, AustraliaReceived 22 September 2004; received in revised form 6 April 2005; accepted 6 June 2005AbstractA comparison ong>ofong> sediments and associated macrobenthos at sites sampled within 20 fish farm leases distributed acrosssoutheastern Tasmania indicated major natural changes along a regional cline. Introduced taxa were strongly represented in thefauna, comprising 45% ong>ofong> total macrong>ofong>aunal biomass. Large differences were evident between sites affected by different levelsong>ofong> organic farm waste. Sites located adjacent (b10 m) to farm cages possessed significantly depressed sediment redox levels, adominance ong>ofong> capitellid and dorvilleid polychaetes, and low macrong>ofong>aunal species richness. Subtle impacts extended across farmlease areas in the form ong>ofong> depressed redox potential at 40 mm depth and changes to the macrobenthic community, including aprevalence ong>ofong> the dogwhelk Nassarius nigellus and a paucity ong>ofong> the heart urchin Brissus sp. and the maldanid polychaetesAsychis sp. and Rhodine sp. Minor farm ong>effectsong> were also evident at sites sampled 35 m outside farm lease boundaries, mostnotably as elevated population numbers ong>ofong> the polychaete Terrebellides sp., bivalve Mysella donaciformis and heart urchinEchinocardium cordatum. Amongst the univariate metrics examined, redox potential at 40 mm depth and the ratio ong>ofong> bivalvesto total molluscs provided the most sensitive indicators ong>ofong> farm impacts, with the latter metric relatively insensitive to spatialvariation between locations within the region studied.D 2005 Elsevier B.V. All rights reserved.Keywords: Capitella sp.; Environmental monitoring; Crustaceans; Impact assessment; Introduced species; Finfish ong>aquacultureong>; Molluscs;Polychaetes; Redox1. Introduction* Corresponding author. University ong>ofong> Tasmania, GPO Box 252-49, Hobart, Tasmania 7001, Australia. Tel.: +61 3 6226 7632; fax:+61 3 6226 2745.E-mail address: (G.J. Edgar).An important consequence ong>ofong> caged fish ong>aquacultureong>is an environmental footprint in the form ong>ofong> wasteorganic matter, which partly disperses in the watercolumn and partly deposits on the seabed. The ong>scaleong> ong>ofong>0022-0981/$ - see front matter D 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.jembe.2005.06.003

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 71this and other impacts ong>ofong> fish farming remains animportant public, managerial and scientific issue.Although quite different opinions have been expressedabout both the magnitude and type ong>ofong> fishfarm ong>effectsong>, consensus exists that a gradient ong>ofong> diminishingimpact extends out from cages, that the magnitudeong>ofong> impact varies from location to location, and thatin the worst case scenario, the sediment environmentunder cages can become anoxic (Iwama, 1991; Karakassiset al., 1999). Most environmental studies haveconcentrated on severe impacts under and immediatelyadjacent to cages (Findlay et al., 1995; Hargrave et al.,1997; Mazzola et al., 2000). While such impacts can beextreme, they are also highly localised and are lesslikely to be significant at regional ong>scaleong>s than any subtleong>effectsong> that extend hundreds ong>ofong> meters out from farmleases.Severe impacts under cages are ong>ofong> major concern tong>ofong>arm managers because ong>ofong> the relationship with thehealth ong>ofong> caged fish, while less obvious impacts atdistance from cages may be ong>ofong> greater concern toenvironmental managers (e.g., localised eutrophicationand dinong>ofong>lagellate blooms). Under-cage ong>effectsong>are generally easier to determine and can be monitoredwith little scientific input using a variety ong>ofong>approaches. By contrast, a scientifically-credible toolremains to be developed to allow routine assessmentong>ofong> the more subtle broad-ong>scaleong> ong>effectsong> ong>ofong> fish farming.This study assessed the efficacy ong>ofong> a variety ong>ofong>physical and biological metrics in discriminating fishfarm impacts at different distances from cages, withemphasis on the more distant ong>effectsong>. A novel aspectong>ofong> our study is that it uses individual fish farm leasesas replicate sampling units to encompass regionalvariation across southeastern Tasmania. With fewexceptions (notably Hargrave et al., 1997; Carroll etal., 2003), previous field studies ong>ofong> fish farm impactshave been confined to one or two farm leases (e.g.,Brown et al., 1987; Ritz et al., 1989; Tsutsumi et al.,1991; Johannessen et al., 1994; Findlay et al., 1995;Mazzola et al., 2000; Kempf et al., 2002). Becauselocal environmental conditions can greatly influenceresults at individual farm leases, generalisations fromsuch studies should be viewed with caution.In addition to its scientific interest, this study waspartly initiated in response to a management need forbetter information on the environmental ong>effectsong> ong>ofong>finfish farms. Regulations associated with the operationong>ofong> Tasmanian finfish farms stipulate bno unacceptablevisual, chemical or biological impact on thebenthos 35 m beyond the boundary ong>ofong> the ong>marineong>farming lease area. Relevant environmental parametersmust be monitored in the lease area, 35 mfrom the boundary ong>ofong> the ong>marineong> farm lease area andat any control site(s)Q (Crawford, 2003). Consequently,identification ong>ofong> environmental parameters that canbe routinely measured and are sensitive to distant fishfarm impacts remains a high management priority.Useful monitoring metrics can either be physicochemicalor biological, their value depending on bothsensitivity and cost-effectiveness. Nonetheless, abioticmetrics have little value unless they are ultimatelyrelated to organisms. A reduction in redox potentialfrom 100 to 0, for example, has little meaning unlessadditional information is available on how such achange affects the plants and animals in the area.Here we investigate several key biotic and abioticmetrics recommended in previous studies for assessingenvironmental impacts ong>ofong> fish farm waste. Thesemetrics include carbon and nitrogen levels, stableisotope ratios, redox potential, and aspects ong>ofong> macrong>ofong>aunalcomposition (Ye et al., 1991; Pohle et al., 2001;Wildish et al., 2001; Carroll et al., 2003).2. Methods2.1. Sampling protocolsEnvironmental and biological data were obtainedin collaboration with ong>salmonidong> farm operators between1997 and 1999 from 20 separate farm leaselocations distributed across a 120 km span ong>ofong> coast insoutheastern Tasmania. Farm leases varied in size andproduction capacity but were typically 20–25 ha, with6–20 cages (26–36 m in diameter) stocked per lease.Cages were periodically moved within farm leaseareas, with rotation times ong>ofong> 3–36 months. Depths ong>ofong>sites studied ranged from 7 m to 47 m, with mean ong>ofong>20 m. Farm leases were nested within four managementregions: (i) lower Huon Estuary, (ii) lower D’EntrecasteauxChannel, (iii) upper D’EntrecasteauxChannel, and (iv) Tasman Peninsula (Fig. 1).Sites sampled within each farm lease were locatedby differential GPS and categorised by level ong>ofong> farmimpact as (i) dcageT sites, located b10 m from stocked

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 73in Table 2 ong>ofong> Edgar (1990). For large animals retainedby the 4 mm sieve, mean AFDW values specific toeach taxon were applied, as measured for 18 commonspecies from additional samples collected across theregion. Conversion values were obtained from a meanong>ofong> four individuals per species retained on a 4 mmsieve. These were individually weighed after dryingat 60 8C, and again after ashing at 550 8C. The fewindividuals (b10% ong>ofong> total) retained by a 4 mm sievewith no measurement ong>ofong> large animal AFDW wereassumed to possess similar biomass to a species withsimilar body dimensions.A Craib corer was used alongside the Van Veengrab at each site to collect triplicate sediment cores(43 mm in diameter) for analysis ong>ofong> sediment properties.Redox potential was measured in millivolts at thesurface ong>ofong> the sediment and at 10 mm and 40 mmdepth below the sediment surface. The standard potentialong>ofong> the Ag/AgCl reference cell ong>ofong> the probe was199 mV. Calibration and functionality ong>ofong> the meterwere checked before each test using a redox buffersolution (220 mV at 25 8C). Measurements were madewithin 3 h ong>ofong> the samples being collected.After redox measurements were completed, twosub-samples were separated from each Craib core.The top 30 mm ong>ofong> sediment was collected in a vialfor analysis ong>ofong> organic content, %C, %N, stable isotoperatios and particle size. The next 70 mm was usedfor particle size analysis only. The top 30 mm sampleswere homogenised in the laboratory and one halfseparated and oven dried at 60 8C. Once this subsamplewas dry, it was ground to a fine powder and a20 mg sample taken for stable isotope analysis. Theremainder ong>ofong> the sample was placed in a porcelaincrucible and weighed, then heated to 450 8C in amuffle furnace for 4 h and reweighed. Loss ong>ofong> weighton ignition was regarded as organic content, calculatedas percentage dry weight.Dried 20 mg sub-samples from each sedimentcore were weighed and analysed for %carbon, %nitrogen,y 13 C and y 15 N by the CSIRO Division ong>ofong>Marine Research, Hobart. Combustion and oxidationwere achieved at 1090 8C and reduction at 650 8C.Samples were not acidified prior to analysis, placingemphasis on accuracy ong>ofong> nitrogen rather than carbondeterminations.The two samples comprising the top 100 mm ong>ofong>each sediment core were combined for particle sizeanalysis. To eliminate depth bias, the 30–100 mmcomponent was homogenised and one half discarded,a proportion equal to the sample removed from the 0–30 mm sample earlier for organic content analysis. Ameasuring cylinder was firmly filled with the samplematerial, and any excess levelled ong>ofong>f with a ruler.Material was then washed through a stack ong>ofong> sieves(4 mm, 2 mm, 1 mm, 500 Am, 250 Am, 125 Am and63 Am). The content ong>ofong> each sieve was drained andtransferred to the measuring cylinder, commencingwith the coarsest fraction and working through tothe finest. The cumulative volume in the measuringcylinder was recorded after the content ong>ofong> each sievewas transferred. The fraction b63 Am in diameter wascalculated by difference from 100%.2.2. Univariate analysesThe ong>effectsong> ong>ofong> fish farming activity on the sedimentenvironment and macrobenthos were initiallyassessed using F-tests associated with analysis ong>ofong>variance (ANOVA). In order to avoid spatial confoundingthrough the difference in number ong>ofong> replicatesites sampled per farm lease, data were aggregated asthe mean value for each ong>ofong> the four levels ong>ofong> impact(cage, farm lease, compliance and reference) at eachfarm lease location. Although the precision ong>ofong> estimatesong>ofong> the true mean will vary with the number ong>ofong> sitessampled within the farm lease, mean data points areincorporated into statistical tests as replicate informationhence such differences in precision should notaffect outcomes ong>ofong> general statistical tests, other thanslightly increasing the residual error term and reducingstatistical power. Farm lease location was included as ablocking factor in the ANOVA model, in order toensure that variation between farm lease locations didnot overwhelm the signal associated with farm ong>effectsong>.The null hypothesis tested was that no differenceexisted between sites grouped into the four impactlevels across the range ong>ofong> farm leases. When this globalhypothesis was rejected, then comparisons were sequentiallyundertaken between (i) reference sites andcage sites, (ii) reference sites and farm lease sites, and(iii) reference sites and compliance sites. In each case, asignificant result initiated the testing ong>ofong> the more distantlevel ong>ofong> impact. The null hypothesis examined was thatno difference existed between sites grouped by farmimpact.

74G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90Response variables assessed included 10 sedimentand 26 macrong>ofong>aunal metrics considered potentialmarkers ong>ofong> farm activity (Table 1). Data forsome metrics were transformed for ANOVA (squareroot or arcsin depending on the type ong>ofong> heteroscedasticitydetected using boxplots). Interpretation ong>ofong>ANOVA results requires caution, given the largenumber ong>ofong> tests undertaken and likelihood ong>ofong>Type I statistical errors. Nevertheless, we did notutilise Bonferoni correction because the number ong>ofong>Type II errors thereby introduced (as a consequenceong>ofong> the large reduction in power) would have greatlyTable 1Metrics examined as potential markers ong>ofong> farm activityMedian particle size (/)%Silt–clay content (particles b63 Am)Redox potential at sediment surfaceRedox potential at 10 mm depthRedox potential at 40 mm depth%C%Ny 13 Cy 15 N%Organic matter, measured as loss on ignitionTotal number ong>ofong> species per sampleMean biomass per individual animal within a sampleMean macrong>ofong>aunal abundanceDominance a calculated using abundance dataEvenness b calculated using abundance dataIntroduced taxa as percentage ong>ofong> total faunal abundanceCapitellid polychaetes as percentage ong>ofong> total faunal abundanceCrustaceans as percentage ong>ofong> total faunal abundancePolychaetes as percentage ong>ofong> total faunal abundanceGastropods as percentage ong>ofong> total faunal abundanceBivalves as percentage ong>ofong> total faunal abundanceEchinoderms as percentage ong>ofong> total faunal abundanceBivalves as percentage ong>ofong> total mollusc abundanceMean macrong>ofong>aunal biomassDominance a calculated using biomass dataEvenness b calculated using biomass dataIntroduced taxa as percentage ong>ofong> total faunal biomassCapitellid polychaetes as percentage ong>ofong> total faunal biomassCrustaceans as percentage ong>ofong> total faunal biomassPolychaetes as percentage ong>ofong> total faunal biomassGastropods as percentage ong>ofong> total faunal biomassBivalves as percentage ong>ofong> total faunal biomassEchinoderms as percentage ong>ofong> total faunal biomassBivalves as percentage ong>ofong> total mollusc biomassa Percentage contribution ong>ofong> species with highest density.b Inverse Simpsons Index (=1/A(n i /N) 2 where n i is density ong>ofong> ithspecies and N is total sample abundance).exceeded the 1.8 (=360.05) Type I errors predictedfor the same set ong>ofong> analyses.2.3. Multivariate analysesIn the analysis ong>ofong> baseline conditions, PrincipalCoordinate Analysis (PCA), as calculated by the CanonicalAnalysis ong>ofong> Principal Coordinates (CAP) programong>ofong> Anderson (2003), was used to identify faunalrelationships between ong>ofong>f-farm sites, including associationswith environmental metrics. To avoid ong>effectsong>associated with farms, only 35 m compliance andreference data were included in this analysis. Triplicatesample data were aggregated as a mean value foreach site, with three sites per farm lease location usedin the PCA to indicate the ong>scaleong> ong>ofong> between site withinlease ong>effectsong>. Where more than three reference andcompliance sites were sampled at a location, compliancesites were selected at random and added toreference sites to make up three sites.For PCA, dsitespeciesT matrices for both abundanceand biomass data were compiled for taxa thatoccurred at more than one site and which possessed atotal abundance N3 individuals or a total biomass N3mg. Data were fourth root transformed to reduce theinfluence ong>ofong> the most abundant taxa, and the similaritymatrix calculated using the Bray–Curtis index becauseong>ofong> the robustness ong>ofong> this statistic to zero-inflated datasets, following recommendations ong>ofong> Faith et al. (1987)and Clarke (1993).Relationships between biological and environmentaldata sets were assessed by calculating Pearsoncorrelation coefficients between abiotic metrics andeach ong>ofong> the first three principal coordinate axes. Abioticmetrics examined included the ten sedimentmetrics listed in Table 1, plus ddepth,T and ddistanceTfrom the farm lease located furthest upstream in theHuon Estuary. The ddistanceT metric represented anindirect measure ong>ofong> estuarine influence and also actedas a grouping variable for farm lease locations withinthe four regions.Faunal differences associated with farm impactwere analysed using Canonical Analysis ong>ofong> PrincipalCoordinates (CAP) (Anderson, 2003), a constrainedordination procedure that initially calculates unconstrainedprincipal coordinate axes, followed by canonicaldiscriminant analysis on the principal coordinatesto maximise separation between sites grouped accord-

78G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90Number ong>ofong> speciesDensity (m -2 )Dominance (%)60504030201004000300020001000080604020****Crustaceans (%)Gastropods (%) Polychaetes (%)*604020080 *6040200302010 ** **0080Simpson’s Index2015105Bivalves (%)604020*Introduced taxa (%total)Capitellids (%)0504030210806040200*Reference Compliance Farm***CageEchinoderms (%)Bivalves (% molluscs)050403020100806040200*Reference Compliance Farm****CageFig. 4. Boxplots showing median, box edges at first and third quartiles, and outlying data points, for number ong>ofong> species per sample and sedimentmetrics involving abundance ong>ofong> faunal components.

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 792550Simpson’s Index Dominance (%) Biomass (g.m -2 ) Mean biomass (µg)201510501510508060402015105****Bivalves (%) Gastropods (%) Polychaetes(%) Crustaceans (%)40302010080604020080604020040302010****Capitellids (%) Introduced taxa (%total)0806040200403020100***Reference Compliance Farm*CageBivalves (% molluscs) Echinoderms (%)03020100*40 * 806040200Reference Compliance Farm**CageFig. 5. Boxplots showing median, box edges at first and third quartiles, and outlying data points, for sediment metrics involving biomass andmean biomass per individual animal.

80G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90Table 3Probability values associated with F-tests for ANOVAs involving farm impact as independent factor, and farm lease location as blocking factorMetric Farm impact Farm lease locationAll Cage Lease Compliance All Cage Lease ComplianceNumber ong>ofong> species b0.001 0.002 0.299 b0.001 0.329 0.003Mean animal biomass 0.267 0.001Total abundance 0.002 0.015 0.584 b0.001 0.112 0.013Total biomass 0.033 0.381 0.222 0.832Dominance (abundance) b0.001 b0.001 0.908 0.408 0.686 0.030Dominance (biomass) b0.001 0.001 0.319 0.108 0.177 0.319Simpson’s Index (abundance) a b0.001 0.001 0.394 0.009 0.696 0.022Simpson’s Index (biomass) a 0.001 0.002 0.768 0.001 0.327 0.03Introduced taxa/total abundance a 0.003 0.056 0.001 0.429Introduced taxa/total biomass a 0.209 0.241Capitellids/total abundance b0.001 b0.001 0.094 0.053 0.457 0.223Capitellids/total biomass b0.001 0.006 0.274 0.672 0.537 0.528Crustaceans/total abundance 0.175 b0.001Crustaceans/total biomass 0.039 0.072 0.049 0.085Polychaetes/total abundance b0.001 b0.001 0.122 0.013 0.190 0.347Polychaetes/total biomass 0.538 0.898Gastropods/total abundance a 0.009 0.008 0.101 0.001 0.010 0.650Gastropods/total biomass a 0.031 0.024 0.069 0.023 0.189 0.458Bivalves/total abundance b0.001 0.002 0.161 0.025 0.458Bivalves/total biomass 0.001 0.008 0.050 0.106 0.297 0.364 0.549 0.559Echinoderms/total abundance a 0.008 0.035 0.430 b0.001 0.036 0.249Echinoderms/total biomass a b0.001 0.026 0.132 0.159 0.224 0.486Bivalves/mollusc abundance b b0.001 b0.001 0.035 0.008 0.003 0.164 0.241 0.022Bivalves/mollusc biomass b b0.001 0.002 0.004 0.039 0.005 0.111 0.262 0.012Initial analysis for each metric involved all four levels ong>ofong> impact (reference, compliance, farm lease, cage; df =3), followed by sequentialcomparisons ong>ofong> reference versus cage, reference versus farm lease, and reference versus compliance sites when preceding test was significant(a =0.05). Location df =16 for abundance metrics and 14 for biomass metrics. Residual df =36 (abundance) and 32 (biomass) for four-level tests(dallT), and 12 (abundance) and 10 (biomass) for pairwise tests.a Data square root transformed.b Data arcsin transformed.Nearly all biological metrics varied significantly( p N0.05) between different levels ong>ofong> farm impact(Table 3); the exceptions were mean animal biomassper individual sampled, proportional biomass ong>ofong> introducedtaxa, proportional abundance ong>ofong> crustaceans,and proportional biomass ong>ofong> polychaetes. In all significantcases, variation between sites was largelydriven by differences between cage and less-impactedsites. Significant differences were evident betweenreference and cage sites for all metrics with significantfour-level trends, with the exceptions ong>ofong> total biomass,proportional abundance ong>ofong> introduced taxa, and proportionalbiomass ong>ofong> crustaceans.The only biological metrics to differ significantlybetween farm lease and reference sites were proportionalbiomass ong>ofong> bivalves and ratio ong>ofong> bivalves tototal molluscs. The latter, as calculated using bothbiomass and abundance data, also differed significantlybetween compliance and reference sites.Most biological metrics varied significantly betweenfarm lease locations (Table 3). However, therewas no evidence ong>ofong> significant variation attributable tolocation for total biomass, the Dominance Index, proportionalabundance and biomass ong>ofong> capitellids, proportionalbiomass ong>ofong> introduced taxa, and proportionalbiomass ong>ofong> polychaetes, bivalves and echinoderms.Introduced taxa represented a large proportion ong>ofong>the total fauna across all sites examined, comprising10% ong>ofong> total animals collected, 45% ong>ofong> total macrong>ofong>aunalbiomass, and 84% ong>ofong> total mollusc biomass(Fig. 6). The New Zealand screw shell Maoricolpusroseus occurred in very high biomass at a few sites,dominating general analyses not standardised for site.Maoricolpus roseus contributed 82% ong>ofong> total mollusc

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 81Abundance (m -2 )14001200100080060040020004IntroducedNativeBiomass (g.m -2 )3210Reference Compliance Farm CageFig. 6. Relationships between mean (+SE) abundance and biomass ong>ofong> introduced and native species at sites subjected to different levels ong>ofong> farmimpact.biomass, while the European clam Corbula gibba(1.5% ong>ofong> total mollusc biomass) and East Asianclam Theora lubrica (0.7% ong>ofong> total mollusc biomass)also dominated individual sites.The total abundance ong>ofong> introduced species variedsignificantly between farm lease locations whenassessed using ANOVA with site as a blocking factorand square root transformation (df =17/38, F =5.15,p b0.001) but not between farm impact levels (df =3/38, F =2.57, p =0.07). By contrast, the total biomass ong>ofong>introduced species varied between impact levels(df = 3/33, F =3.88, p =0.018) but not between farmleases (df =15/33, F =1.46, p =0.18). No significantdifference was detected between reference and cagelocations in the biomass ong>ofong> introduced species.3.3. Faunal patterns ong>ofong> distribution in the absence ong>ofong>farmingPrincipal component analysis (PCA) ong>ofong> ong>ofong>f-farmmacrong>ofong>aunal abundance and biomass data sets producedsimilar patterns ong>ofong> site relationships (Fig. 7).The first two components explained 29.9% ong>ofong> totalvariance amongst 212 species, and 31.1% ong>ofong> varianceamongst 167 species, for abundance and biomassdata sets, respectively. Sites in the Huon estuary, upperD’Entrecasteaux Channel, and Tasman Peninsula generallyseparated from each other, indicating distinctiveregional faunas, whereas sites in the lower D’EntrecasteauxChannel overlapped with sites in otherregions. Variation between the compliance and referencesites within a farm lease was similar to variationbetween farm leases within a region in manycases, and for one farm lease in the lower Channel,variation between the three sites analysed encompassedvariation between the three other regions.Environmental metrics that exhibited strong correlationswith the first two principal coordinate axeswere highly inter-correlated. Metrics generally variedalong the estuarine cline from the Huon estuary to theopen sea, with coarser sediments, lower organic matter,higher y 15 N, and lower sediment redox potentialoccurring in a seaward direction. However, the faunalseparation between the upper Channel and TasmanPeninsula regions was perpendicular to this cline,indicating that factors additional to those examinedin the study were important in influencing benthicpatterns. Water depth apparently contributed little to

82G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90PCO20. PeninsulaUpper ChannelLower ChannelHuonABsiltredox-0.5particleorganic00.5Amphiuraδ15N elandiformisdistance -0.50.5Parathyasira resupina Lysilla jennacubinaeLumbrineris sp.CallianassalimosaC0.5VenericardiabimaculataKalliapseudes sp.0 0.5Echinocardiumcordatum-0.2-0.5-0.5TipimegusthalerusPCO2-0.3-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5PCO10. Lumbrineris sp.bimaculataLysilla jennacubinaedistance0.5Artachamella Clymenella sp.dibranchiataδ15N Callianassalimosa0.500.5-0.5TipimegusAmphiurathaleruselandiformisTerrebellides sp.Echinocardium-0.5 cordatum-0.4-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5PCO1Fig. 7. Site relationships shown as plots ong>ofong> the first two principal components calculated by PCA using abundance (A) and biomass (D) datasets. Sites investigated in different regions are shown with different symbols, with the three sites analysed for each farm lease surrounded byellipses. Correlations between the first two principal components and environmental metrics are shown for abundance (B) and biomass (E) datasets, as are the highest species correlations between principal components and faunal abundance (C) and biomass (F). Correlations betweenprincipal components and depth were very low (b0.15) and are not shown. Redox measurements from different sediment levels (0, 10 and 40mm depth) generated similar correlations; hence, only results for surface sediments are shown.observed faunal differences between sites, with lowcorrelation coefficients for the three major principalcoordinates (b0.15).Species that possessed the highest correlations betweenPCA axes and animal density were the brittle starAmphiura elandiformis, the ghost shrimp Callianassalimosa and the polychaete Terrebellides sp., whichwere strongly associated with Huon estuary sites; theamphipod Tipimegus thalerus and heart urchin Echinocardiumcordatum, which predominantly occurredong>ofong>f Tasman Peninsula; and the polychaetes Lumbrinerissp., Lysilla jennacubinae, Artachamella dibranchiataand Clymenella sp, the bivalves Venericardiabimaculata and Parathyasira resupina, and the tanaidKalliapseudes sp., which were primarily present in theupper D’Entrecasteaux Channel.3.4. Effects ong>ofong> farming on the macrobenthic communityResults ong>ofong> the PCA for sites subjected to differentlevels ong>ofong> farm impact are presented in Fig. 8 for abundancedata relating to 197 common species. The firstprinciple coordinate axis explained 18.9%, and thesecond 15.4%, ong>ofong> total variation. Analysis ong>ofong> biomassdata produced almost identical patterns so results havenot been presented.Sites subjected to different levels ong>ofong> farm impactoverlapped considerably in faunal composition, otherthan cage sites, which tended to group together in theupper right ong>ofong> Fig. 8A. Most sediment metrics possesseda relatively high (N0.5) correlation with the firsttwo canonical axes, with both redox potential and y 15 Nresponding differently to the estuarine cline (Fig. 8B).

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 83PCO26040200-20-40-600.20.1ReferenceComplianceFarmCage-40-200PCO12040A60organic-0.5siltparticleredox00.5Bδ15N-0.50.5depthdistance0.5Nemertean sp.1-0.50.50 0.5Capitella sp.MaoricolpusCallianassaroseus.limosa CorbulaAmphicteis sp.Amphiura gibba -0.5Natatolana sp.elandiformisTheora lubricaNemertean sp.2Lysilla jennacubinae Lumbrineris sp.Ennucula obliquaD E FCTerrebellides sp. Echinocardium cordatumNassarius nigellus0.5CAP20redox-0.5siltparticleAmphiura elandiformis0 0.5Callianassa limosa0 0.5distance-0.5Ennucula obliquaCapitellia sp.-0.1δ15N-0.5Brissus sp.Asychus sp.-0.5-0.2-0.2-0.100.1CAP10.20.30.4Fig. 8. Macrong>ofong>aunal relationships between sites categorised by farm impact shown as plots ong>ofong> the first two principal components calculatedby PCA using abundance data (A). Correlations between the first two principal components and environmental metrics are shown (B), as arethe highest species correlations between principal components and faunal abundance (C). Redox measurements from different sediment levels(0, 10 and 40 mm depth) generated similar correlations; hence, only results for surface sediments are shown. Comparable results ong>ofong> CAPanalysis with farm impact as grouping variable are also shown (D), as are correlations between environmental (E) and species abundancedata (F).Three introduced molluscs, Maoricolpus roseus, Theoralubrica and Corbula gibba, were included amongstspecies showing high correlations with principal coordinateaxes (Fig. 8C).Farm impact groups generally separated along thefirst two axes ong>ofong> canonical space following CAP analysis.Cage sites tended to cluster in the lower right ong>ofong>Fig. 8D, farm lease sites and compliance sites overlappedin the upper left, and reference sites were locatedin the lower left. Notable exceptions to this patternwere one farm lease site and two compliance sites thatgrouped with reference sites in the lower left ong>ofong> thefigure.Species with densities most strongly correlated withthe first two CAP axes were Capitella sp. (R =0.87),which was strongly associated with cage sites, Terrebellidessp. (R =0.68), which was strongly associatedwith farm lease and compliance sites, Amphiura elandiformis(R =0.61) and Callianassa limosa (R =0.57),which were negatively associated with cage sites, andTable 4Results ong>ofong> CAP dleave-one-out allocation ong>ofong> observations to groupsTprocedure for four farm impact groupsKnown Allocated groups Total %CorrectReference Compliance Farm CageleaseReference 9 5 4 0 18 50Compliance 3 5 7 0 15 33Farm lease 1 4 8 1 14 57Cage 1 2 0 8 11 73

84G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90Nassarius nigellus (R =0.53), which was negativelyassociated with reference sites (Fig. 8F).The dleave one out allocation ong>ofong> observations togroupsT procedure (Anderson and Willis, 2003) indicatedthat cage sites formed the most coherent group,with 75% accuracy in assignment ong>ofong> sites to correctfarm impact group (Table 4). Farm lease sites weregenerally categorised appropriately, although 29%were misclassified as compliance sites. Almost half(47%) ong>ofong> compliance sites were inaccurately categorisedas farm lease sites, while another 20%were misclassified as reference sites. Half ong>ofong> allreference sites were misclassified as farm lease orcompliance sites. Despite the misclassifications, assignmentto groups was significantly better thanrandom ( p b0.001).When CAP analysis was run on the reduced data setong>ofong> reference and cage sites only, differences betweengroups remained highly significant ( p =0.01), withonly three instances ong>ofong> misclassification, all whencage sites were grouped with reference sites. The polychaeteCapitella sp. showed an exceptionally highpositive correlation with cage ong>effectsong> (Table 5). Thesix species showing highest positive correlations withTable 5Species showing strongest positive and negative Pearson correlations (R) with principal CAP axis for two-level comparisons ong>ofong> reference siteswith cage sites, farm lease sites and compliance sitesNegative associationsPositive associationsSpecies Taxon R Species Taxon RCage/ReferenceAmphiura elandiformis E 0.669 Capitella sp. P 0.805Lysilla jennacubinae P 0.599 Dorvilleid sp. P 0.470Callianassa limosa C 0.582 Malacoceros tripartitus P 0.437Ennucula obliqua B 0.544 Neanthes cricognatha P 0.403Thyasira adelaideana B 0.497 Hesionid sp. P 0.398Theora lubrica B 0.477 Dorvillea sp. P 0.331Nemocardium thetidis B 0.444 Jassa sp. C 0.323Hiatella australis B 0.443 Ceratonereis pseudoerythraeensis P 0.292Kalliapseudes sp. C 0.436 Nassarius nigellus G 0.287Nemertean sp. 0.417 Pectinaria sp. P 0.238Farm lease/referenceBrissus sp. E 0.506 Nassarius nigellus G 0.563Asychis sp. P 0.482 Echinocardium cordatum E 0.372Rhodine sp. P 0.404 Cyclaspis caprella C 0.328Ennucula obliqua B 0.403 Capitella sp. P 0.325Amphiura elandiformis E 0.360 Callianassa ceramica C 0.308Nemocardium thetidis B 0.340 Terrebellides sp. P 0.296Venericardia bimaculata B 0.322 Mysella donaciformis B 0.284Diopatra sp. P 0.322 Corbula gibba B 0.273Ampeliscid sp. C 0.311 Flabelligerid sp. P 0.268Venerid sp. B 0.287 Chaetozone setosa P 0.261Compliance/referenceLumbrineris sp. P 0.249 Terrebellides sp. P 0.643Montacutid sp. B 0.223 Mysella donaciformis B 0.594Tawera gallinula B 0.203 Echinocardium cordatum E 0.553Ampeliscid sp. C 0.197 Paraonides sp. P 0.540Phoxocephalid sp. C 0.188 Mysid sp. C 0.435Amygdalum beddomei B 0.159 Prionospio multipinnulata P 0.422Maldanid sp. P 0.157 Glycera sp. P 0.417Melitid sp. C 0.150 Armandia sp. P 0.404Ennucula obliqua B 0.150 Maoricolpus roseus G 0.402Fulvia tenuicostata B 0.143 Nassarius nigellus G 0.401Taxonomic groups are listed as crustaceans (C), polychaetes (P), gastropods (G), bivalves (B) and echinoderms (E).

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 85cage impacts were all polychaetes, whereas five ong>ofong> theten species showing highest negative correlations werebivalves. If species with correlation coefficients N0.4are arbitrarily considered to possess strong associations,then four species possessed strong positive correlationswith cage ong>effectsong> compared to 11 species withstrong negative correlations.Farm lease sites also possessed faunas that weresignificantly different to those at reference sites whenassessed using a two-level CAP comparison ( p =0.036); however, the same analysis did not indicatesignificant differences for comparisons ong>ofong> referencesites with compliance sites ( p =0.12). The latter outcomewas probably a Type II statistical error, resultingfrom substantial variation between sites being includedas residual noise in the one-factor two-level CAPcomparisons. This error is indicated by two-wayNPMANOVA, where variation attributable to siteswas partly separated from residual error by includingregion as a factor (four levels: Huon, lower D’Entrecasteaux,upper D’Entrecasteaux and Tasman Peninsula,with three sites within each). NPMANOVA revealedhighly significant multivariate differences in faunalassemblages between reference and compliance sites(df = 1/16, F =5.425, p b0.001), and between regions(df = 3/16, F =2.362, p b0.001). Interaction betweenfactors was also significant (df =3/16, F =2.590,p b0.001), indicating that the magnitude ong>ofong> differencebetween farm lease and compliance sites varied betweenregions.The species showing strongest negative correlationswith farm lease sites were the heart urchinBrissus sp. and the maldanid polychaetes Asychissp. and Rhodine sp. (Table 5). Another heart urchinEchinocardium cordatum was highly positively correlatedwith farm lease sites, as was the snail Nassariusnigellus.Densities ong>ofong> several species, most notably the polychaeteTerrebellides sp. and the bivalve Mysella donaciformis,were highly positively correlated in thereference/compliance site comparisons. Ten specieswere strongly positively associated with compliancesites (R N0.4), whereas all negative correlations ong>ofong>species abundance involving the principal CAP axiswere weak (R b0.25). Thus, differences between complianceand reference sites were attributable more toincreased abundance ong>ofong> particular species rather thanloss ong>ofong> species.4. DiscussionAnalysis ong>ofong> the regional ong>ofong>f-farm data set indicatedthat macrong>ofong>aunal assemblages in the southeastern Tasmanianregion varied with an ong>ofong>fshore gradient fromthe lower Huon estuary to Tasman Peninsula. Numerousenvironmental variables corresponded with thisgradient—including sediment particle size, sedimentorganic matter and y 15 N; however, the primary determinantsong>ofong> faunal distribution patterns remain to beidentified. Marine sites with similar sediment propertiesin the upper D’Entrecasteaux Channel and TasmanPeninsula showed major differences in faunas, indicatingthat factors unexamined in the present study, perhapsincluding wave exposure and historical factors,also contributed substantially to regional variation.Depth did not correspond in any substantive way toobserved patterns.As found in other studies worldwide (Brown et al.,1987; Johannessen et al., 1994; Karakassis et al.,1999), the sediment environment beside fish farmcages, and its associated macrobenthic assemblage,differed greatly from that present at undisturbed referencelocations. Differences between cage and referencesites were ong>ofong> sufficient magnitude to be detectedby the majority ong>ofong> metrics investigated.With some notable exceptions, biotic patterns observedalong gradients ong>ofong> organic enrichment in Tasmaniawere broadly consistent with generalisedpatterns noted in the Northern Hemisphere (Pearsonand Rosenberg, 1978). We only found extreme ong>effectsong>,such as samples being anoxic or azoic (other than foranimals recently detached from overhead nets suchas mussels, Jassa spp. and Caprella spp.), in a fewreplicate samples obtained beside cages (unpublisheddata). Such dgrossly pollutedT conditions (sensu Pearsonand Rosenberg, 1978) did not, however, extendto all three replicates at a site, hence aggregatedsamples at cage sites were more generally referableto the dpollutedT category proposed by Pearson andRosenberg (1978). As in the Northern Hemisphere(Wildish et al., 1999; Brooks and Mahnken, 2003)and in recent local studies (Crawford et al., 2002;Macleod et al., 2004b), polluted sites possessed severelydepressed redox levels and were typicallydominated by Capitella sp., although dorvilleid polychaetesand the capitellid Heteromastus sp. were alsolocally abundant.

86G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90Within the farm lease area but distant from cages,faunal assemblages in Tasmania were characterised bya dominance ong>ofong> the dogwhelk Nassarius nigellus, thebivalve Mysella donaciformis, the polychaete Terrebellidessp., the heart urchin Echinocardium cordatumand the introduced bivalve Corbula gibba. This intermediatemacrobenthic assemblage partially correspondswith the dtransitoryT category ong>ofong> Pearson andRosenberg (1978); however, those authors consideredthe genera Terrebellides and Echinocardium to beindicative ong>ofong> dnormalT rather than dtransitoryT conditions.Perhaps conditions at reference sites studied inthe Northern Hemisphere were naturally affected byhigher organic loadings than Tasmanian referencesites, regardless ong>ofong> fish farm influences. Macleod etal. (2004a) also noted geographical differences betweenthe level ong>ofong> impact apparent in sediment chemistryin Tasmania when compared with similar studiesundertaken in the Northern Hemisphere. Althoughbroad similarities existed in ecological ong>effectsong> ong>ofong> fishfarming regardless ong>ofong> country, in Tasmania detectableong>effectsong> were associated with considerably lower levelsong>ofong> chemical degradation than elsewhere (Macleod etal., 2004a).dTransitoryT faunas in southeastern Tasmania weredivisible into two assemblage types. Assemblages athigher levels ong>ofong> organic loading within lease areaswere characterised by fewer species, whereas at compliancesites outside lease boundaries assemblageswere characterised by the addition ong>ofong> species. Thesepatterns are indicated by results presented in Table 5,with more macrobenthic species possessing strongpositive than negative CAP axis correlations at cageand farm lease sites, and the contrary pattern occurringat compliance sites.Amongst the species positively associated withfarm lease sites was the introduced European clamCorbula gibba. However, despite the relative abundanceong>ofong> C. gibba at farm lease sites, metrics combiningdensities ong>ofong> introduced taxa were inconsistentin their response to farm impact treatments, in largepart because Maoricolpus roseus, a gastropod thatdominated biomass analyses, was little affected byimpact treatments. Although the total abundance ong>ofong>introduced species varied between the four farmimpact treatments (Table 3), differences betweentreatments were insufficiently large to generate significantresults in pairwise tests. Thus, in contrast togeneral predictions that introduced taxa are attractedto disturbed habitat types (Occhipinti-Ambrogi andSavini, 2003), exotic species did not respond consistentlyas a group to organically-enriched benthichabitats in our study.Univariate biotic metrics investigated here weregenerally more sensitive to ong>effectsong> ong>ofong> farming thanabiotic metrics. Only one abiotic metric studied, redoxpotential at 40 mm depth, was sufficiently sensitive todetect differences between farm lease and referencesites, and this metric also indicated differences insediment properties between compliance and referencesites. Historic reductions in redox levels resultingfrom farming probably persisted as a signal for longerat depth than at the sediment surface, where sedimentre-oxygenation by resuspension, bioturbation and diffusionoccurs more rapidly.Given that the majority ong>ofong> abiotic metrics wereunable to detect subtle ong>effectsong> ong>ofong> farming, and thatmajor ong>effectsong> can be identified in real time byobserving features such as a white surface mat ong>ofong>the bacteria Beggiatoa sp. in underwater video footage(Crawford et al., 2001, 2002; Macleod et al.,2004a,b), then particle size distribution, y 13 C, y 15 N,%silt/clay, %C, %N and total organic matter wouldnot appear to be effective tools for monitoring fishfarms. Macleod et al. (2004b) reached a similarconclusion.A caveat associated with the above statement, thatthe sediment metrics listed above do not provide highlyuseful tools for monitoring, is that the utility ong>ofong> metricsinvolving residual error cannot be discounted becauseinter-correlations between metrics were very common.For example, total organic matter was highly correlatedwith sediment particle size (R =0.79), hence changes inorganic matter associated with fish farms were possiblyoverwhelmed by variation in particle size betweensites, whereas examination ong>ofong> residual deviation fromthe general organic matter/particle size regressioncould show important trends. We did not examineresidual metrics here because ong>ofong> the large number ong>ofong>metrics already included in our study and the increasedlikelihood ong>ofong> Type I overfitting errors.In addition to sediment metrics described here, weinvestigated sediment prong>ofong>iles at sites by recordingdepth ong>ofong> major changes in sediment appearance withintransparent Craib cores; however, a discrete surfacesulphide layer was rarely observable in our samples,

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 87making quantification ong>ofong> patterns difficult. Consistentdifferences in sediment prong>ofong>iles were not clearly distinguishablebetween samples, other than in anoxicsamples collected adjacent to cages. Consequently, wealso consider that sediment prong>ofong>ile imaging – a frequently-recommendedtechnique (Nilsson and Rosenberg,1997; Karakassis et al., 2002) – would beunlikely to reveal subtle ong>effectsong>.It is important to note that a lack ong>ofong> statisticalsignificance for a particular metric does not equateto an absence ong>ofong> change in sediment properties, onlythat the power ong>ofong> tests based on our experimentaldesign was insufficient to detect change. Because ong>ofong>substantial patchiness in impact across farm leaseareas due to water currents and cage rotation, withsome sample sites positioned in areas from wherecages had recently been moved, and other sites locatedaway from cages near the farm lease boundary,variance between sites within a farm lease was high.Such variation in level ong>ofong> organic loading at differentcage, farm and compliance sites presumably reducedthe power ong>ofong> statistical tests, particularly for generalregional tests.The large number ong>ofong> farm leases investigated neverthelessallows reasonable confidence in our inferencesfor significant results. Although we lackedbaseline data and therefore could not exclude thepossibility ong>ofong> spatial confounding, systematic differencesbetween farm lease and reference sites that wereunrelated to farm impact were unlikely. This conclusionis supported by different suites ong>ofong> species havingbeen identified as affected by farming in cage, farmlease, and compliance site pairwise comparisons withreference sites. If reference sites were systematicallyanomalous, with particular species present in anomalouslyhigh or low abundances, then those specieswould consistently be highlighted in all pairwise comparisonsinvolving reference sites.The most sensitive ong>ofong> the univariate biotic metricsin our study, the ratio ong>ofong> bivalves to total molluscs,was significantly lower at compliance sites than referencesites regardless ong>ofong> whether calculated usingabundance or biomass data. Brooks and Mahnken(2003) also identified molluscs as sensitive indicatorsong>ofong> organic enrichment ong>effectsong>, however, they did notdistinguish between bivalves and gastropods. A likelyreason for the disproportionate decline in bivalves,which coincided with decreased redox potential at40 mm sediment depth, is that anoxic sediments atdepth provided unsuitable habitat. Benthic gastropodstend to be more associated with the sediment surfaceand therefore less affected by conditions deep in thesediment than bivalves. In the case ong>ofong> detritivorousspecies such as Nassarius nigellus, gastropods canalso be positively associated with moderate organicloading.An advantage ong>ofong> the bivalve:mollusc ratio overredox potential at 40 mm as a univariate metric ong>ofong>subtle farm ong>effectsong> across the southeastern Tasmanianregion is that the former is relatively insensitive tochanges in farm lease location. No significant differencesin the bivalve:mollusc ratio were detected betweenfarm lease locations for pairwise comparisons ong>ofong>reference and farm lease sites, or reference and cagesites. Thus, a single threshold value could potentiallybe used region-wide to identify subtle farm impactsrather than comparisons requiring local reference sites,as is necessary in the case ong>ofong> redox potential. Furtherassessment is required ong>ofong> regional variation in thebivalve:mollusc ratio, given that the more powerfulfour-level ANOVA indicated significant differencesbetween farm leases. Such variation was driven bychanges in the density ong>ofong> gastropods, whereas abundanceand biomass ong>ofong> bivalves did not differ significantlybetween farm lease locations (Table 3).Biomass data possessed no clear advantage overabundance data for any univariate metric examined,probably because the fortuitous inclusion ong>ofong> a singlelarge individual in a sample added greatly to variabilityin biomass between samples, reducing the powerong>ofong> tests. Moreover, in contrast to predictions thatorganically-enriched sediments would be dominatedby small, rapidly-growing individuals (Warwick,1986), no significant variation was detected in meanbiomass ong>ofong> individuals in different treatments. Consequently,given the considerable extra effort required tocompile biomass data, we suggest that this effortcould be better utilised in the inclusion ong>ofong> extra samplesin a monitoring program.The most sensitive univariate indicator ong>ofong> thebroad-ong>scaleong> ong>effectsong> ong>ofong> fish farming in southeasternTasmania is likely to be total density ong>ofong> an aggregatedset ong>ofong> the species that are positively correlated withfarm impacts less total density ong>ofong> a set ong>ofong> negativelycorrelated species. None ong>ofong> the species listed in Table5 occurred commonly at all farm lease sites; hence no

88G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90single species could be used as a regional indicator ong>ofong>subtle farm ong>effectsong>.An example ong>ofong> a regional combined species metricis the total abundance ong>ofong> Nassarius nigellus, Echinocardiumcordatum and Terrebellides sp., three speciesstrongly positively associated with farm impacts, lessabundance ong>ofong> Amphiura elandiformis, Nemocardiumthetidis and Ennucula obliqua, three species negativelyassociated. When this indicator is assessed usingthe ANOVA design described in Table 3 for complianceversus reference sites, then no difference isdetected between farm lease locations (df = 10/10,F =1.64, p =0.22) but a significant effect ong>ofong> farmingis evident (df =1/10, F = 15.25, p =0.003). Note, however,that the latter probability value is inaccurategiven that it relates to a variate identified a posteriorifrom the same data set used for probability tests.Appropriate testing ong>ofong> any identified metric requiresan independent data set.The practical value ong>ofong> a sensitive univariate indicatoris that it can be used for routine environmentalmonitoring. Tasmanian ong>marineong> farm regulations requirethat any impacts extending from farm leaseareas not be dunacceptableT when measured at compliancelocations 35 m outside the farm lease boundary.Unacceptable impacts are currently defined asoccurring when redox levels decrease by 150 mVfrom baseline/reference values and/or when there isa threefold or greater increase in organic matter. Currentregulations are thus at the evidentiary end ong>ofong> thecontinuum from a need for clear evidence ong>ofong> farmimpacts to a precautionary approach. Changes at compliancesites must be similar or greater than meanvalues observed immediately adjacent to cages to beconsidered unacceptable.A key role ong>ofong> benthic monitoring is to allow fishfarm managers to assess whether sites are approachinganoxia, while recovery from grossly impacted conditionsmay also be important when assessing rotationperiods for cages. Some samples collected within farmleases in our study were presumably from patchesrecovering from impact, as well as trending towardsimpact. The seabed within each farm lease compriseda mosaic ong>ofong> patches with different histories ong>ofong> disturbance,including some fallowed patches that recentlypossessed cages directly above.In a recent study ong>ofong> recovery/degradation rates attwo farm lease sites in southern Tasmania that werelocated in different environments but with standardisedproduction levels (Macleod et al., 2004b),marked differences were observed in benthic impactsdepending on the intensity ong>ofong> farming (stocking density,feed input and duration) and geographical location.Macleod et al. (2004b) suggest that althoughmany faunal features are similar regardless ong>ofong> successionaldirection, subtle faunal differences in assemblagesexist that are specific to recovery or degradation.Comparison ong>ofong> benthic assemblages at farm leaseand compliance sites also allows some inference to bemade on the trajectory ong>ofong> benthic response to disturbance.Compliance sites were unlikely to be in a recoveryphase given that they were located distant fromthe sources ong>ofong> farm pollution, and characteristicallyexhibited gains but little loss ong>ofong> species. Loss ong>ofong> speciesmay be largely associated with high rather than moderateinputs ong>ofong> organic matter, in which case assemblagesrecovering from organic waste impacts wouldpossess fewer species than assemblage progressingtowards grossly polluted status.Given the large number ong>ofong> species examined inthe present study, and variety ong>ofong> abiotic and bioticmetrics investigated, Type 1 errors possibly occurred,where the contribution ong>ofong> taxa or metric to observedpatterns has been overemphasised. Clearly, additionalresearch is urgently needed to confirm patterns – orotherwise – particularly with respect to assessment ong>ofong>impact and recovery over time. Such analysis shouldinclude investigation ong>ofong> broad-ong>scaleong> change over thepast few years, a period ong>ofong> accelerating transformationin local benthic communities (Edgar and Samson,2004). Notably, Macleod et al. (2004b) foundthat introduced taxa played a more conspicuous rolein faunal patterns than evident in our study. Thismay reflect changes in the overall community compositionmore recent than our sampling, with anincreasing influence ong>ofong> introduced taxa (Edgar etal., in press).AcknowledgementsLogistical assistance and funding for data collectionwere provided by Tassal Operations Ltd., Huon AquacultureCompany Pty Ltd. and Aquatas Pty Ltd. Commitmentto the study by Trevor Dix (Tassal OperationsLtd.) is particularly appreciated, as are comments on

G.J. Edgar et al. / J. Exp. Mar. Biol. Ecol. 327 (2005) 70–90 89the draft manuscript by Colin Shepherd, GrahamWoods and Dom O’Brien. The experimental designwas largely based on recommendations ong>ofong> ChristineCrawford for regulatory sampling protocols. Stableisotope analyses were carried out by Andy Revill ong>ofong>CSIRO Marine Research, and the map was provided byVanessa Halley. The manuscript was also improved,thanks to the comments ong>ofong> three anonymous referees.[RH]ReferencesAnderson, M.J., 2001. A new method for non-parametric multivariateanalysis ong>ofong> variance. Austral Ecol. 26, 32–46.Anderson, M.J., 2003. CAP: A FORTRAN Computer Program forCanonical Analysis ong>ofong> Principal Coordinates. Department ong>ofong>Statistics, University ong>ofong> Auckland, Auckland, N.Z. 14 pp.Anderson, M.J., Robinson, J., 2003. Generalised discriminant analysisbased on distances. Aust. N. Z. J. Stat. 45, 301–318.Anderson, M.J., Willis, T.J., 2003. 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