Marine Pollution Bulletin 62 (2011) 1427–1436

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Marine Pollution Bulletin

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Long-term trends in benthic habitat quality as determined by Multivariate AMBIand Infaunal Quality Index in relation to natural variability: A case studyin Kinsale Harbour, south coast of Ireland

Robert Kennedy ⇑, Wallace Arthur, Brendan F. KeeganZoology Department, Ryan Institute, School of Natural Sciences, National University of Ireland, Galway, Ireland

a r t i c l e i n f o

Keywords:Water Framework DirectiveBenthic invertebratesSoft-bottom communitiesMixed modellingIntercalibrationLong-term change

0025-326X/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.marpolbul.2011.04.030

⇑ Corresponding author. Tel.: +353 91 493215; fax:E-mail address: bob.kennedy@nuigalway.ie (R. Ken

a b s t r a c t

Benthic Ecological Quality Ratios (EQR) are important tools for assessing the ecological status of coastaland transitional water bodies. Here, we use spatial and time-series data from Kinsale Harbour, Ireland toexamine the effects of sample processing methodologies on the outputs of two EQRs: Multivariate AMBI(M-AMBI) and Infaunal Quality Index (IQI).

Both EQRs were robust to changes in sieve size from 1 mm to 0.5 mm, and to changes in the taxa iden-tified in spatial calibration. Both EQRs classified habitat quality in Kinsale as generally Good or High withno evidence of significant change over the time series (1981–2006). IQI classified the ecological status ashigher than M-AMBI.

There was a significant relationship between IQI and M-AMBI in spatial calibration, but no significantrelationship between them in time series. Further research into the behaviour of EQRs in relation to nat-ural variability over long time-scales is needed to discriminate anthropogenic impacts reliably.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

The European Water Framework Directive (WFD; 2000/60/EC)has established a framework for the protection and improvementof all European surface and ground waters including transitionaland coastal waters. The final objective is to achieve at least ‘goodwater status’ for all water bodies by 2015. Each Member State isrequired to assess the Ecological Status (ES) of water bodies. Statuswill be assigned through the assessment of biological, hydromor-phological and physico-chemical quality elements. Data obtainedfrom monitoring are compared to reference (undisturbed) condi-tions to derive an Ecological Quality Ratio (EQR). These ratios areexpressed as a decimal value between zero and one, with ‘high’status represented by values close to one and ‘bad’ status by valuesclose to zero. The EQR scales are divided into five ecological statusclasses (high, good, moderate, poor, and bad) by assigning anumerical value to each of the class boundaries. In coastal andtransitional waters soft bottom benthic macrofauna is one of theimportant and frequently used elements in determining habitatquality (Pearson and Rosenberg, 1978; Borja et al., 2000; Daueret al., 1993).

Biological quality elements that must be included in the ESassessment of a water body include ‘the level of diversity and

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+353 91 525005.nedy).

abundance of invertebrate taxa’ and the proportion of ‘distur-bance-sensitive taxa’ (Borja et al., 2007). Several methodologieshave been proposed by member states for the status assessmentof the benthic component (Borja et al., 2000, 2004a,b, 2007; Prioret al., 2004; Rosenberg et al., 2004; Simboura and Zenetos, 2002;van Hoey et al., 2007; Teixeira et al., 2010). All of these methodol-ogies have focused upon the proportion of disturbance-sensitivetaxa. The proposed methodologies continue to be intercalibratedbetween the member states and within ecoregions and habitats(Borja et al., 2007; van Hoey et al., 2007; Borja et al., 2009;Simboura and Argyrou, 2010), with particular importance beingattached to agreement between measures on the ‘‘Good/Moderate’’boundary. In coastal waters there has been a good level ofagreement between classifications using different EQRs, but issuesremain with transitional and estuarine waters (Simboura andReizopoulou, 2008; Dauvin, 2007; Elliott and Quintino, 2007).

Most data used for deriving and intercalibrating between indi-ces and EQRs has been taken from spatial disturbance gradients(Borja et al., 2008a,b, 2007; van Hoey et al., 2007). Temporal data-sets often carry the confounding and interacting effects of season-ality, inter-annual variability, operator effects and methodologicalchanges such as the use of differing sieve sizes for the retaining ofmacrofauna (Pinto et al., 2009). Given that the purpose of the EQRsis long term monitoring, their behaviour in long term time series,and their robustness to these issues are very pertinent (Krönckeand Reiss, 2010).

1428 R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436

Here, we use a 23-year time series macrobenthic data set fromthe south coast of Ireland to assess the performance of the UK andIreland Infaunal Quality Index (IQI) over time. We begin with aspatial calibration study to compare the three sample processingmethodologies used in the time series. This is followed by analysisof the time series data in relation to local weather and regionalclimatic events. Because very little data have been publishedconcerning IQI, we compare the spatial and temporal results withthose of the Multivariate AZTI Marine Biotic Index (M-AMBI;Muxika et al., 2007a), the use of which has been reportedfrequently in the literature.

2. Study Area

Kinsale Harbour is a south-facing embayment on the southcoast of Ireland (Fig. 1). The river Bandon, with a mean daily flowrate of 15 m3 s�1 discharges into the Harbour via a series of townsand villages with a total population of <10,000. This carries consid-erable amounts of terrestrial plant debris form the forested catch-ment area, and slightly depresses off-bottom salinities in the innerpart of the study area. Kinsale town has a baseline population of ca.2500, increasing substantially in summer months. During theperiod of this study there were only primary sewage treatmentfacilities in Kinsale, and the effluents were discharged at severalpoint outfalls distributed in the Inner Harbour. This is no longerthe case at the time of writing. This study was commenced beforea pharmaceutical company installed a subtidal outfall pipe in themiddle of the study area in 1982.

For the purposes of the benthic element of the monitoring pro-gramme, the Harbour was divided into five Areas; see Fig. 1. Thisdivision was partly based on habitat type and partly on proximityto the plant outfall or the Bandon discharge. Basic habitat informa-tion for these areas is given in Table 1. The southern regions out-side of the sampling areas are rocky. The bottom communitiescorrespond well to the EUNIS level 4 biotopes assigned (Connoret al., 2004). Dineen and Neiland (1986) described the macrofaunalcommunities of Kinsale sensu (Thorson, 1957). Area A is the

Fig. 1. Benthic grab sampling locations in Kinsale Harbour and Approaches (1981–200stations).

deepest site and is characterised by the ophiuroid Amphiura filifor-mis and the bivalve Kurtiella bidentata in muddy fine sand. Area Bcontains the plant outfall, and has the coarsest sediments andstrongest currents in the study area. It is characterised by veneridbivalves. Area C is fine sand characterised by the bivalve Fabulinafabula and polychaetes of the genus Magelona. Area D is character-ised by Fabulina and bivalves of the genus Abra. Area E contains theKinsale sewage outfalls, two yachting marinas, and a commercialfishing pier that is base for approximately ten trawlers. It is char-acterised by Abra, polychaetes of the genus Nephtys and the poly-chaete Lanice conchilega. Kinsale Port is also based in Area E andapproximately 150,000 ton of cargo pass through the port annu-ally, mostly bulk animal feeds but fertilizers, timber and buildingmaterials are also common. The tidal range is between 4.3 mspring and 3.2 m neap, while peak tidal currents range between1.5 and 3.0 knots. The flushing time for the Harbour is <1 day(Muylaert and Raine, 1999). The bottom sediments in the Harbourare often turbated by wind induced waves and tidal currents(Dineen and Neiland, 1986).

3. Methods

Sampling was performed annually in summer (May toSeptember) from 1981 to 2006 with the exceptions of 1986,1989 and 1990. Kinsale Harbour was divided into five Areas(A–E, described above and in Fig. 1). Two random stations withineach area were sampled. At each station two 0.1 m2 Day grabswere taken and the macrofauna identified were summed. Threedifferent sample processing regimes were used over the study per-iod. From 1981 to 1993, and 2004 to 2006, all fauna retained on a1 mm mesh were identified. From 1994 to 1998 polychaetes, mol-luscs and echinoderms retained on a 0.5 mm mesh were identified.From 1999 to 2003 all fauna retained on a 0.5 mm mesh wereidentified.

Because sample processing can strongly affect the results ofmacrobenthic analyses (Couto et al., 2010; Gage et al., 2002; Jameset al., 1995; Kingston and Riddle, 1989; Pinto et al., 2009;

6). The Areas denoted A–E were sampled randomly (two Day grabs at each of two

Table 1Basic habitat information for the sampling areas in Kinsale Harbour.

Area Mean depth (m) Sediment type Mean salinity EUNIS Level 4

A 18 Muddy sand 34 Circalittoral muddy sandB 14 Gravelly sand 34 Infralittoral coarse sedimentC 12 Sand 32 Infralittoral muddy sandD 10 Muddy sand 30 Infralittoral muddy sandE 10 Muddy sand 29 Infralittoral muddy sand

Sediment type is sediment classification sensu Folk and Ward (1957). EUNIS Level 4 is biotope classification sensu Connor et al. (2004).

R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436 1429

Thompson et al., 2003), a calibration study between all methodswas carried out using data collected in 2004. In the calibrationsurvey, 28 stations were sampled (Fig. 1). Six Day grab sampleswere taken at each station, i.e. two independent Day grabs for eachsample processing methodology.

Two multimetric indices were calculated to determine benthichabitat quality sensu the Water Framework Directive (WFD). TheUK and Irish Infaunal Quality Index (IQI) version 4 was calculatedusing a proprietary tool in Microsoft Excel developed by the UKEnvironment Agency. This includes truncation of the species lists,and spelling and synonym standardization. IQI was calculated byEq. (1)

IQI ¼0:38� 1�AMBI=7ð Þ

1�AMBI=7ð Þmax

� �� �þ 0:08� ð1�k0 Þ

1�k0ð Þmax

� �� �þ 0:54� S0:1

S0:1max

� �� �� 0:4

� �

0:6ð1Þ

where: AMBI is the AZTI Marine Benthic Index (Borja et al., 2000);1 � k0 is Simpson’s Evenness Index; S0.1 is log10 (number of species);max parameters are the maximum reference values for the habitat.

The multimetric boundaries for Water Framework Directiveclassification of EUNIS A5.2 and A5.3 marine sublittoral sandsand muds were used in this study as shown in Table 1.

Multivariate-AMBI (M-AMBI; Muxika et al., 2007a) was calcu-lated using a free software tool available from AZTI (http://www.azti.es) and guidelines from the authors concerning the cal-culation of AMBI (Borja and Muxika, 2005; Muxika et al., 2007b).M-AMBI involves ordination of samples based on the values ofAMBI, number of species and Shannon-Wiener diversity, followedby Factor Analysis to determine the distance of the sample fromvirtual ‘‘High’’ and ‘‘Bad’’ endpoints. The classification boundariesused were the standard boundaries determined by intercalibrationof benthic ecological status assessment between European states(Borja et al., 2007; EC, 2008), shown in Table 2.

In the 2004 study, a two-way ANOVA using Sample processingand Area as fixed factors was carried out, using each diversity ormulitmetric index as individual response variables in turn. Regres-sion analyses were carried out using the multimetric indices asresponse variables and the component indices used in their calcu-lation as predictor variables. To investigate the role of the diversityindices determining the variance in the mulitmetric indices,variance decomposition (Zuur et al., 2007) was performed.

The agreement in classification between methods using differ-ent sample processing techniques was determined using a Kappaanalysis (Cohen, 1960; Landis and Koch, 1977) applying the meth-od presented in Borja et al. (2007) and Simboura and Reizopoulou(2008). This methodology applies a weighting to misclassifications

Table 2Classification boundaries for mulitmetric indices IQI and M-AMBI.

Classification boundary IQI M-AMBI

Good–high 0.75 0.85Moderate–good 0.64 0.55Poor–moderate 0.44 0.38Bad–poor 0.24 0.20

to down-weight the importance of misclassification between adja-cent classes, while misclassifications between non-adjacent classesare assigned considerable importance.

Regression analysis of IQI and M-AMBI was carried out using IQIas the response variable, M-AMBI as a continuous factor, Area as afixed factor and Sample Processing as a fixed factor. This was fol-lowed by ANOVA and optimal model selection using the AkaikeInformation Criterion (AIC; Akaike, 1974) and the Variance Infla-tion Factors (VIF) to inform forwards and backwards variable selec-tion to maximise explained variance in the model while reducingmodel complexity (Zuur et al., 2007; Kutner et al., 2004).

Temporal analyses were carried out on IQI and M-AMBI. Partialautocorrelation function (PACF) plots were made for IQI and M-AMBI for each Area using lags of up to 16 years. As the sample PACFwas not significantly different from zero at the 5% significance levelfor any plot, autoregressive time series analyses were not used. IQIand M-AMBI were regressed against Time in years post 1981 as acontinuous factor and against Area as a fixed factor with modelselection as outlined above. Where significant effects of interactionbetween Time and Area were found, the EQRs of each area was sep-arately regressed against Time to determine which areas wereshowing different temporal patterns.

The relationship of M-AMBI and IQI in time series was deter-mined in two ways. The first was to use simple bivariate linearregression between the two EQRs. The second method was to usea linear mixed effects model that was fitted using a restricted max-imum likelihood test (REML). IQI was the response variable, M-AMBI and Area were fixed effects while Time was a random effect.

To explain the identified trends in IQI and M-AMBI, each wasseparately regressed against a suite of environmental variablesincluding the value of the North Atlantic Oscillation (NAO) indexfor the previous winter, the mean daily discharge of the BandonRiver for the previous 50, 100 and 150 days, mean daily rainfallfor the previous 50 days, and mean daily wind speed for the previ-ous 50 days. Optimal model selection was carried out.

4. Results

Two-way ANOVA of the 2004 calibration study (Table 3)showed that there was no significant effect of Sample Processingon either M-AMBI, IQI or any of their component indices. Therewas a significant effect of Area on IQI and AMBI. Variance decom-position analyses (Table 4) showed that both mulitmetric indicescould be modelled very effectively in a linear manner withoutinteraction using the component indices as predictor variables (ad-justed r2 = 0.979 and 1.000 for IQI and M-AMBI respectively). IQIand M-AMBI both use AMBI and Number of species (S) in their cal-culation. AMBI and S both explained more of the variance in IQIthan in M-AMBI. Simpson’s Evenness Index (1 � k0) explained thesmallest portion of the variance in IQI. Shannon–Wiener Diversity(H0) explained marginally more variance in M-AMBI than the othercomponents, but most of the variance in M-AMBI was shared var-iance caused by interaction of the components. In IQI, most of thevariance was accounted for by the individual components, with amuch smaller amount of shared variance.

Table 3Results of Two-way ANOVA using sample processing and area as factors and diversity or mulitmetric indices as individual response variables in turn. P-values are shown for eachindex.

p

Df IQI 1 � k0 S AMBI H0 M-AMBI

Sample processing 2 ns ns ns ns 0.069 0.091Area 4 <0.001 0.078 ns <0.001 0.054 0.054Sample processing � area 8 ns ns ns ns ns nsResiduals 66

IQI is the Infaunal Quality Index, 1 � k0 is Simpson’s Evenness Index, S is number of species, AMBI is the AZTI Marine Benthic Index, H0 is Shannon–Wiener diversity, M-AMBI isthe Multivariate AZTI Marine Benthic Index. ns: p > 0.10.

Table 4Variance decomposition analysis for the spatial calibration study showing thevariance in (a) Infaunal Quality Index (IQI) and (b) Multivariate AZTI Marine BenthicIndex (M-AMBI) accounted for by the component indices used in their calculation.

r2 Variance explained by

(a) IQIS + (1 � k0) + AMBI 0.979 0.979S + (1 � k0) 0.574 AMBI 0.405(1 � k0) + AMBI 0.726 S 0.253S + AMBI 0.914 (1 � k0) 0.065

Shared variance 0.256

(b) M-AMBIS + H0 + AMBI 1.000 1.000S + H0 0.832 AMBI 0.168S + AMBI 0.828 H0 0.172H0 + AMBI 0.877 S 0.123

Shared variance 0.537

S is number of species, (1 � k0) is Simpson’s evenness index, AMBI is the AZTIMarine Benthic Index, H0 is Shannon–Wiener diversity.

Table 5Kappa values, percentage mismatch and agreement between Infaunal Quality Index(IQI) and Multivariate AZTI Marine Benthic Index (M-AMBI) calculated from n = 2independent Day grabs using different sampling methodologies.

Kappa % Mismatch Agreement

IQI 20041 mm

EPM 0.521 11.11 Moderate0.5 mm 0.57 7.41 Good

M-AMBI 20041 mm

EPM 0.971 0.00 Almost perfect0.5 mm 0.888 0.00 Almost perfect

2004 IQI v M-AMBIIQI

M-AMBI (1 mm) 0.8895 0.00 Almost perfectM-AMBI (All samples) 0.7983 3.70 Very good

Timeseries IQI v M-AMBIIQI

M-AMBI (1 mm and 0.5 mm) �0.092 17.4 No agreementM-AMBI (All samples) �0.102 17.2 No agreement

1 mm = all fauna identified, sieved on a 1 mm mesh; 0.5 mm = all fauna identified,sieved on a 0.5 mm mesh; EPM = Echinoderms, Polychaetes and Molluscs identified,sieved on a 0.5 mm mesh.

1430 R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436

Bivariate regression analyses of IQI using M-AMBI as an explan-atory variable for the spatial calibration data showed a highly sig-nificant negative relationship between the two multimetric indicesthat accounted for 12.9% of the variance in IQI (Fig. 2a). Results ofthe weighted Kappa analysis of the agreement in classification be-tween the Sample Processing methodologies are shown in Table 5.In IQI, the agreement between the normal all fauna identified1 mm samples (1 mm) and the samples where only echinoderms,polychaetes and molluscs retained on a 0.5 mm mesh were identi-fied (EPM) was moderate. Agreement between 1 mm samples andsamples where all fauna retained on a 0.5 mm mesh were identi-fied (0.5 mm) was good. In M-AMBI, agreement between both1 mm and EPM samples and between 1 mm and 0.5 mm sampleswas almost perfect. When the classifications assigned by the twomultimetrics were compared, the agreement between 1 mm IQIand 1 mm M-AMBI samples was almost perfect, while agreementbetween all IQI samples and all M-AMBI samples was very good.

Fig. 2. Bivariate regression analysis of Infaunal Quality Index (IQI) and Multivariate AZ0.264(M-AMBI) adjusted r2 = 0.129, p < 0.001 and (b) the time-series data collected ip < 0.001.

In time series, bivariate regression of IQI and M-AMBI in(Fig. 2b) showed a very similar highly significant relationship tothe calibration study, explaining 8.96% of the variance in IQI. Cor-responding Kappa analysis (Table 5) showed that for both the1 mm samples and all samples collected there was no significantagreement between classifications assigned by M-AMBI and IQIto the time-series data.

For the calibration study, multiple regression of IQI usingM-AMBI, Area and Sample Processing as factors (Table 6) showedthat M-AMBI and Area had significant effects on the value of IQIwhile Sample Processing had no significant effect. The optimummodel found showed a strongly positive highly significant relation-ship between M-AMBI and IQI at Area A (which was assigned the

TI Marine Biotic Index (M-AMBI) for (a) the 2004 spatial calibration: IQI = 0.981–n Kinsale Harbour 1981–2006: IQI = 0.954–0.224(M-AMBI); adjusted r2 = 0.0896,

Table 6(a) Regression analysis of IQI against M-AMBI from spatial calibration study and (b)output of model selection procedure using sample processing and area as factors.

(a) Response: IQI spatial(2004)

Df SS MS F p

M-AMBI 1 0.45655 0.45655 175.838 <0.001Sample processing 2 0.00764 0.00382 1.471 0.237Area 4 0.13371 0.03343 12.874 <0.001Sample processing:area 8 0.00458 0.00057 0.220 0.986Residuals 65 0.16877 0.0026

(b) IQI = 0.33794 + 0.59342 (M-AMBI) + area coefficient (= 0 for Area A)Coefficient S.E. t p

(Intercept) 0.33794 0.03362 10.053 <0.001M-AMBI 0.59342 0.0486 12.21 <0.001Area(B) 0.09338 0.01715 5.444 <0.001Area(C) 0.02287 0.01665 1.373 0.174Area(D) 0.04376 0.01753 2.496 <0.05Area(E) �0.03213 0.01721 �1.867 0.066

Residual standard error: 0.049 on 75 degrees of freedom.Multiple R2: 0.765, adjusted R2: 0.750.F-statistic: 49.09 on 5 and 75 DF, p < 0.001.

R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436 1431

mean relationship by the model) with positive fixed effects atAreas B, C and D, and a negative fixed effect at Area E. This modelexplained 75.0% of the variance in IQI.

The mixed effects model of IQI using M-AMBI and Area as fac-tors and Time as a random effect produced a different result tothe spatial data. There was a non-significant negative effect of M-AMBI and a significant effect of Area, i.e. there was no relationshipbetween IQI and M-AMBI over Time (see Table 7).

Fig. 3(a) shows IQI plotted over time for each Area. Of the 115IQI EQRs shown on the plot, all are High or Good except for AreaA in 1994 and 1998, and Area E in 2002 and 2003. These four EQRswere classified as Moderate. Regression of IQI against Time in yearspost 1981 (Table 8a) revealed that both Time and Area had signif-icant effects. The optimal model derived showed a slightly negativehighly significant relationship between IQI and Time, while theArea factor added a positive coefficient to IQI in all areas other thanArea A. This model explained 26.0% of the variance in IQI.

M-AMBI is plotted for each area over time in Fig. 3(b). Most EQRwere classified as Good (96 out of 115), with only four samplesclassified as High, three from Area A and one from Area B. Fifteensamples from Areas C, D and E (five, six and four respectively) wereclassified as Moderate. Regression analysis against Time and Areashowed no overall effect of Time on M-AMBI, a highly significanteffect of Area and a significant interaction of Time and Area(Table 8b). Subsequent analysis of the effect of Time on M-AMBIin each Area separately revealed a significant effect of Time onlyin Area E (Table 8c).

Table 7Linear mixed effects model of IQI in Time series using M-AMBI and area as factors andTime as a random effect. The model was fitted using a restricted maximum likelihoodtest (REML).

Random Effects Time Intercept Residual

Std. Dev. 0.0412 0.0499

Fixed Effects M-AMBI + area t-value p-value

Value Std. Error DF

Intercept 0.822 0.048 87 17.002 0.000M-AMBI �0.080 0.063 87 -1.268 0.208Area(B) 0.048 0.016 87 3.078 0.003Area(C) 0.058 0.017 87 3.326 0.001Area(D) 0.056 0.017 87 3.291 0.001Area(E) 0.038 0.016 87 2.353 0.021

The optimum models for IQI and M-AMBI using local weatherdata and NAO index are shown in Table 9 for the whole study areaand Table 10 considering each Area separately. Considering IQI forthe whole study area, there was a significant negative effect ofmean rainfall in the previous 50 days before sampling (Rain50)and mean Bandon discharge for the 150 days before sampling(Q150) and a positive effect of mean daily wind speed for the pre-vious 50 days before sampling (Wind50). There was a significantArea effect. When the Areas were analysed individually, those clos-est to the Bandon discharge showed increasingly strong negativerelationships between freshwater input and IQI. There was no sig-nificant model derived for the two deepest and most marine AreasA and B. Area C shows a weak effect of mean rainfall in the previous50 days before sampling (Rain50) with an adjusted r2 of 0.194. AreaD shows a similar negative effect of Rain50 with a positive effect ofmean daily wind speed for the previous 50 days (Wind50) at ahigher adjusted r2 of 0.246. Area E shows a positive effect ofWind50, a negative effective of mean Bandon discharge for the150 days before sampling and a positive effect of NAO, at an ad-justed r2 of 0.392.

For M-AMBI, the results of the regression analyses are shown inTable 9. When analysing the whole study area, there was a signif-icant Area effect and a significant negative effect of NAO. As for IQI,Areas A and B had no significant effects of weather variables whenthe stations were analysed separately. The effects of weather vari-ables on M-AMBI in the inner Areas were the opposite to the caseof IQI. Area C had a significant negative effect of Wind50 with anadjusted r2 of 0.440. Area D had a significant positive effect ofQ150 with an adjusted r2 of 0.135. Area E had a negative effect ofNAOwin with an r2 of 0.123.

5. Discussion

5.1. Effect of sample processing

The value of diversity indices in macrobenthic communities ishighly dependent on sample size (Clarke and Warwick, 2001)and potentially sensitive to changes in sample processing. Thereis an extensive literature on the effect of sample processing onmacrobenthic data in terms of sieve size (James et al., 1995;Kingston and Riddle, 1989). In fully marine coastal and deep seasettings some studies have reported that abundances returned ona 1 mm mesh are >90% of those returned on a 0.5 mm mesh (Gageet al., 2002; Reish, 1959). Others have recommended the use of a1 mm mesh as the extra information gained in using a 0.5 mmmesh has generally not added power to discriminate between dis-turbed and undisturbed areas (Bishop and Hartley 1986; Ferraroet al., 1989, 1994; Hartley, 1982; Thompson et al., 2003). Thishas not been the case in estuarine sediments, where the 0.5 mmmesh has often returned substantially higher abundances of mac-rofauna (Couto et al., 2010; Pinto et al., 2009; Bachelet, 1990;Schlacher and Wooldridge, 1996). This difference appears to reflectestablished paradigms of macrobenthic succession in relation todisturbance (Pearson and Rosenberg, 1978; Rhoads et al., 1978)where the stressors of salinity fluctuation, organic enrichment,sediment mobility and possible anthropogenic impacts in estuariesselect for stress tolerant species. These are opportunistic, shortlived, small bodied taxa such as oligochaete worms, copepods,nematode worms, small polychaetes and small amphipods that re-quire the use of a 0.5 mm sieve to record their abundance. In mar-ine sediment habitats, dominant macrofauna tend to be largerbodied, longer lived, stress intolearnt species such as errant poly-chaetes, decapod crustaceans, ophiuroids and terebelliid polychae-tes. In the case of Kinsale Harbour all of the Areas sampled wereessentially marine in character, and there was no significant effectof sample processing in the case of any of the indices calculated.

Fig. 3. (a) Infaunal Quality Index (IQI) and (b) M-AMBI EQR scores from Kinsale Harbour and Approaches, 1981–2006.

Table 8Regression analysis of (a) IQI and (b) M-AMBI against Time in years post 1981 andarea. (c) Regression analysis of M-AMBI in Area E using Time as a predictor variable.Similar analyses for Areas A–D produced no significant relationship.

Df Sum Sq. Mean Sq. F value P

(a) IQITime 1 0.0856 0.0856 24.9204 <0.001Area 4 0.07323 0.01831 5.33 <0.001Time:area 4 0.0238 0.00595 1.7324 0.148Residuals 105 0.36066 0.00343

(b) M-AMBITime 1 0.00544 0.00544 0.8711 0.3528Area 4 0.3161 0.07903 12.6485 <0.001Time:area 4 0.07626 0.01907 3.0517 <0.05Residuals 105 0.65602 0.00625

(c) M-AMBI(E) = 0.571 + 0.0053 Time; adjusted R2: 0.193, p < 0.05

1432 R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436

The other issue involved in sample processing in this study isthe range of taxonomic groups identified. Few studies have com-pared the effects of selecting subsets of the fauna for ecologicalassessment to assessing the entire fauna. Warwick (1986) pro-posed the Abundance Biomass Comparison (ABC) technique forclassifying benthic habitats without spatial or temporal reference,based on the paradigm that the distribution of abundance amongstspecies in disturbed communities is dominated by small bodiedopportunists, whilst the distribution of biomass amongst species

is more evenly spread. In undisturbed communities, the abun-dances are relatively evenly spread amongst species, but biomassis dominated by the large bodied stress intolerant species typicalof ‘‘normal’’ habitats sensu (Pearson and Rosenberg, 1978).

In response to criticism that the ABC model was not universallyapplicable, particularly in estuaries (Dauer et al., 1993) and lowsalinity sandflats in the Baltic (Buekema, 1988), Warwick andClarke (1994) pointed out that the ABC response under stress (de-crease in average size of individuals) is most reliable when a sub-stantial portion of the fauna involved are polychaetes.

Olsgard and Somerfield (2000) investigated the use of taxo-nomic levels higher than species and the use of individual majorclasses and phyla (polychaetes, molluscs, echinoderms, and crusta-cean) to discriminate strong stress gradients associated with oilwells in the North Sea. They showed that aggregation to taxonomiclevels higher than species caused very little loss of discriminatorypower, and that the polychaetes alone could be used to determinestrong disturbance effects. This lends some support to the viewthat maintaining the polychaetes and other main higher taxa (mol-luscs and echinoderms) in coastal marine sandy habitats may besufficient to represent overall community structure.

Warwick et al. (2010) investigated the effect of calculatingAMBI using transformed abundance data, biomass data and pro-duction data when compared to a disturbance axis generated frommeta-analysis of several previously described impacts. They foundthat all versions of AMBI calculated provided a better fit to the dis-turbance axis than standard diversity indices, and that AMBI de-

Table 9Output of optimal model selection procedure using local weather data and the North Atlantic Oscillation (NAO) value as predictor variables, and M-AMBI and IQI as the responsevariables.

Coefficient S.E. t p

(a) IQI = 0.767–0.001Rain50 + 0.015 Wind50–0.003 Q150 + AREAIntercept 0.767 0.066 11.688 <0.001Rain50 �0.001 0.000 �3.811 <0.001Wind50 0.015 0.006 2.524 <0.05Q150 �0.003 0.001 �2.147 <0.05Area(B) 0.054 0.018 3.073 <0.01Area(C) 0.070 0.018 3.967 <0.001Area(D) 0.067 0.018 3.782 <0.001Area(E) 0.045 0.018 2.576 <0.05Residual standard error: 0.060 on 107 degrees of freedom, Multiple R2: 0.293, adjusted R2: 0.246, F-statistic: 6.324 on 7 and 107 DF, p < 0.001(b) M-AMBI = 0.743–0.055 NAO + area(Intercept) 0.74299 0.01698 43.762 <0.001NAO �0.05466 0.0272 �2.01 <0.05Area(B) �0.07913 0.02382 �3.322 <0.01Area(C) �0.14897 0.02382 �6.253 <0.001Area(D) �0.13478 0.02382 �5.658 <0.001Area(E) �0.09819 0.02382 �4.122 <0.001Residual standard error: 0.081 on 109 degrees of freedom, Multiple R2: 0.325, adjusted R2: 0.294, F-statistic: 10.49 on 5 and 109 DF, p < 0.001

Rain50 is mean daily rainfall in mm for the 50 days before sampling. Wind50 is the mean wind speed in knots for the 50 days before sampling. NAO is the NAO value for thewinter preceding sampling. Q150 is the mean daily discharge of the Bandon River (m3 s�1) for the 150 days before sampling.

Table 10Output of optimal model selection procedure for each area using local weather data and the North Atlantic Oscillation (NAO) value as predictor variables and IQI (a) and M-AMBI(b) as the response variables.

Area Model Adjusted r2 p

IQI(A) No significant model – –IQI(B) No significant model – –IQI(C) 0.981–0.0017 (Rain50) 0.194 <0.05IQI(D) 0.720–0.0011 (Rain50) + 0.0211(Wind50) 0.246 <0.05IQI(E) 0.479 + 0.0710 (NAOwin) + 0.0453 (Wind50)–0.01446 (Q150) 0.392 <0.01M-AMBI(A) No significant model – –M-AMBI(B) No significant model – –M-AMBI(C) 1.076–0.051(Wind50) 0.440 <0.001M-AMBI(D) 0.535 + 0.009(Q150) 0.135 <0.05M-AMBI(E) 0.650–0.122(NAOwin) 0.123 <0.05

Rain50 is mean daily rainfall in mm for the 50 days before sampling. Wind50 is the mean wind speed in knots for the 50 days before sampling. NAOwin is the NAO value forthe winter preceding sampling. Q150 is the mean daily discharge of the Bandon River (m3 s�1) for the 150 days before sampling.

R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436 1433

rived from moderately (square root) transformed abundance dataprovided the best fit.

Reduced-effort macrobenthic sampling regimes involving theidentification of taxa at levels higher than species have been veri-fied as being sufficient to discriminate disturbance events (Ferraroand Cole, 1990; James et al., 1995; Warwick, 1988a,b, 1993).

While the studies outlined above are focused on different issuesto removing some taxa from the data matrix and identifying theremainder to species level, they do show that the disturbanceresponse in macrobenthic community structure is robust to somedegrees and some types of simplification.

In the case of this study, non-significant effects of sample pro-cessing were determined for non-estuarine sediments. This is con-sidered sufficient evidence to continue to model temporal changein the IQI and M-AMBI EQR without incorporating the effect ofsample processing in this study area.

Another way to examine the effect of sample processing on theoutput of M-AMBI and IQI is to determine the agreement in classi-fication by each index when calculated using the different levels ofsample processing. M-AMBI was very robust to the change in sam-ple processing, giving almost perfect agreement between the threelevels. IQI was less robust, giving moderate agreement (disagreeing3 out of 28 times) between the 1 mm and EPM samples and goodagreement (disagreeing 2 out of 28 times) between 1 mm and0.5 mm samples. Borja et al. (2007) demonstrated a method tointercalibrate between different EQRs to maximise agreement by

optimising the class boundaries, particularly the ‘‘Good/Moderate’’boundary. This has worked well in marine habitats but has hadlimited success in transitional waters (Simboura and Reizopoulou,2008). The agreement between the sample processing methodscould not be improved using this technique in this study, presum-ably because of comparatively small sample size. The shared bestfit, at least for M-AMBI, between the modified data in this studyand the European intercalibration boundaries (Borja et al., 2007;EC, 2008) provides verification that the same outputs were pro-duced by the methods during the calibration study.

5.2. Comparison of indices

IQI is an index that has been rarely reported in the published lit-erature. The version reported in Borja et al. (2007) and Kröncke andReiss (2010), is version 2 and has now been replaced in the UK andROI by version 4. van Hoey et al. (2007) used the same version ofIQI as this study to compare the ecological status of the NorthSea benthos, and compared it to all other member state EQRsand diversity indices. IQI was highly and significantly correlatedwith M-AMBI and with the other EQRs. As in this study, M-AMBIdid not follow the same pattern as AMBI. The variance decomposi-tion showed that much of the variance in IQI in the 2004 study iscaused by changes in AMBI and number of species (S). Changesin the number of sensitive taxa and total number of species havea direct linear effect on IQI. The situation with M-AMBI is different.

1434 R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436

AMBI, S and Shannon-Wiener diversity (H’) all have relatively weakand equal effects on M-AMBI. Most of the variance in M-AMBI iscaused by interaction between the indices or non-linear effects.This is attributable to the factor rotation used in the calculationof M-AMBI, and explains the lack colinearity between M-AMBIand AMBI. Many studies have compared the use of various benthicindices (Diaz et al., 2004; Borja and Dauer, 2008; Rosenberg et al.,2004; Borja et al., 2007; Simboura and Argyrou, 2010) for the pur-poses of establishing monitoring regimes and intercalibration be-tween operators and have sought to harmonise the outputs ofthe various indices as far as possible. Others Kröncke and Reiss,2010; Teixeira et al., 2008; Reiss and Kröncke, 2005) focused onparticular water bodies have stressed the importance of consider-ing multiple indices simultaneously to interpret local spatial heter-ogeneity and natural variability in the form of seasonality andextreme weather events.

Warwick et al. (2010) suggest that AMBI is a more useful toolthan standard diversity measures for discriminating disturbancebecause standard measures (species richness, Shannon diversity,evenness) tend to show maximum diversity at intermediate levelsof disturbance. A far greater proportion of the variance in IQI is di-rectly accounted for by AMBI than is the case for M-AMBI. IQI mayretain more of the robustness of AMBI to ‘‘intermediate distur-bance hypothesis’’ (Grime, 1973) type effects, and have a more lin-ear relationship with disturbance. Conversely, IQI has more of itsvariance accounted for directly by number of species than M-AMBI.This may negate the effects of AMBI somewhat because S will tendto reach its maximum value at intermediate levels of disturbance.In this study we have shown the influence that the componentdiversity indices have on the EQRs in Kinsale Harbour. This willcertainly differ from marine to estuarine habitats, and from onewater body to another. Further study in other areas may revealuseful general trends in these relationships.

In Kinsale Harbour, a spatial calibration study between IQI andM-AMBI showed a significant positive relationship between theEQRs and very good agreement in classification, irrespective ofsample processing technique, once the effects of Area were takeninto account. When the EQRs were compared by simple bivariateregression of all samples from the Harbour, there was a negativerelationship between IQI and M-AMBI. This highlights the impor-tance of local habitat and community type on the values of EQR(Borja and Muxika, 2005).

In time series, the EQRs showed different patterns. Simplebivariate regression showed a very similar relationship betweenIQI and M-AMBI in time series and in the calibration study. How-ever, when the effects of Area were factored in, IQI showed a signif-icant effect of Area and a small but significant decrease over Time,while M-AMBI had a significant effect of Area, no significant effectof Time, and a significant interaction of Area and Time. Furtheranalyses showed that there was a significant negative effect ofTime on M-AMBI only in Area E, the shallowest and most estuarinearea. When the EQRs were compared with a mixed model usingTime as a random effect, there was no significant relationship be-tween M-AMBI and IQI.

The different responses of the EQRs to temporal change are areflection of the proportions of their variances accounted for bytheir component indices, and the responses of these variables tochanging ecological conditions. Multimetric indices such as M-AMBI and IQI have been shown to be less sensitive to interannualand seasonal variability than univariate diversity indices and bioticindices, such as AMBI in the North Sea (Reiss and Kröncke, 2005),while still allowing the detection of extreme weather events suchas very cold winters (Kröncke and Reiss, 2010). When examiningthe effect of long-term variability on a suite of indices includingIQI version 2 and M-AMBI, Kröncke and Reiss (2010) included re-sults for M-AMBI that were far more variable than the correspond-

ing IQI data. M-AMBI classified their stations most often as beingGood, but the classification varied from Poor to High. IQI also var-ied from Poor to High, but was most often classified as High. Theseresults are similar to those of the present study in terms of thegreater variability of M-AMBI and the higher classification of IQI.

While intercalibration in previous studies (Borja et al., 2007;van Hoey et al., 2007) and the results of this study have indicatedgood agreement between IQI and M-AMBI when used on spatialgradients, this has not led to corresponding temporal patterns be-tween the EQRs. This is an important consideration when analysingfor significant change in ecological status, the purpose of devisingEQRs. From the limited amount of published studies of compara-tive temporal change it appears that IQI is a more conservativeindex that is less sensitive to seasonal and natural variation thanM-AMBI.

5.3. Trends in habitat quality in Kinsale Harbour

Habitat quality is classified as Good or High in the vast majorityof samples taken during the time series study. There is no evidenceof any deleterious change over the course of the study, and whatchange has occurred can be related to local weather and regionalclimatic conditions (NAO). When the study area is considered asa whole, there is a small but significant negative effect on IQI offreshwater input in the form of rainfall and discharge of theBandon River. There was a negative effect on M-AMBI of NAO, i.e.M-AMBI decreased with increasing storminess. Both relationshipsaccount for a substantial amount of the variance in the EQRs andare highly significant. When the Areas are considered separately,Areas A and B, the deepest and most marine areas show no signif-icant effects of weather for either EQR. In the case of IQI, there is anincreasingly significant effect of weather and river discharge in theshallower, less marine Areas C, D and E, with r2 increasing in theinner areas. For M-AMBI, there are significant effects of wind atArea C, river discharge at Area B and NAO at Area C.

At the local scale, the effects of the weather parameters on thetwo EQRs were the opposite. In Area E NAO had a negative effect onIQI and a positive effect on M-AMBI. In Area D, river discharge hada positive effect on M-AMBI while in Area E it had a negative effecton IQI. Wind had a negative effect on M-AMBI in Area C but a po-sitive effect on IQI in Areas D and E.

When analysing long term trends in benthic communities it isnecessary to consider more than one measure to gain an overallperspective on the natural variability of the system (Kröncke andReiss, 2010). In this study we have elucidated some of the reasonsfor this. Whether one describes habitat quality in Kinsale Harbouras mostly High or mostly Good is a matter of which EQR is chosen.The response of the EQRs at the Area scale to weather variableswas the opposite, while the response at the whole Harbour scaleappears to be essentially the same, i.e. increasing storminess de-creased the value of the EQR. This highlights the need to gain a suf-ficient number of representative samples when determining EQRs,covering the range of habitat types found in the waterbody (Borjaet al., 2007).

6. Conclusions

Habitat quality in Kinsale Harbour shows no deleterious im-pacts over the 25 year course of this study. What change has takenplace is attributable to weather events and inter-annual variability.IQI and M-AMBI produced results that showed strong agreement ina spatial calibration study, but showed less agreement in timeseries. Natural variability, freshwater input and storm events hadsignificant effects on the value of the EQRs. IQI appears to be moreconservative than M-AMBI, but further study is needed to confirm

R. Kennedy et al. / Marine Pollution Bulletin 62 (2011) 1427–1436 1435

this. The temporal response of the EQRs is scale-dependent andvaried between adjacent stations. Sampling should be avoidedafter extreme weather events if the aim of the study is to discrim-inate anthropogenic impacts. Both EQRs were robust to changes insample processing techniques, which may allow the use of datafrom different sources for applications such as climate change re-search. Further research is needed to establish the behaviour ofEQRs in time series, particularly with regard to natural variability.If the purpose of these measures is to provide a signal of anthropo-genic disturbance, their response to the natural variability overlong time-scales must be established.

Acknowledgements

This work was funded by Eli Lilly, S.A., Dunderrow, Kinsale, Co.Cork Ireland. We thank the staff of Eli Lilly for their continued sup-port for this project. We thank Angel Borja of AZTI for kindly provid-ing us with a MS Excel tool for the Kappa analysis. We thank JohnGalvin and Albert Lawless for technical support through many yearsof sampling. We thank all of the undergraduate students who tookpart in this project. We thank the post graduate students who haveworked on this project: Patricia Dineen, Sinead Neiland, WilliamCrowe, Hazel Allen, Eileen O’Ceidigh, Brendan Ball, Tara Gallagher,Kevin O’Brien and Ian O’Connor. This paper is dedicated to thememory of Brendan F Keegan, mentor to all who worked on thisproject, and principal investigator up to his untimely death in 2004.

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