Pre-Dredging Sediment Assessment Burford Bank, … Sediment Assessment Burford Bank, Dublin on...

Pre-Dredging Sediment Assessment

Burford Bank, Dublin

on behalf of

Dublin Port

Moore Marine Services

Job No: M07D01

Date: November 2008

Author: Robert Kennedy

Client: Jacobs Engineering

Atlantic Resource Management Solutions Caherawoneen, Kinvara, Co. Galway

TABLE OF CONTENTS

1. INTRODUCTION............................................................................................. 3

1.1 Methods ................................................................................................... 3

1.2 Sediments ................................................................................................. 3

1.3 Macrofauna .............................................................................................. 3

1.4 Sediment Profile Imagery .......................................................................... 6

2. RESULTS........................................................................................................ 6

2.1 Sediments ................................................................................................. 6

2.2 Macrofauna .............................................................................................. 7

2.3 Sediment Profile Imagery .........................................................................17

3. OVERALL ASSESSMENT.................................................................................19

4. LITERATURE CITED .......................................................................................20

TABLE OF FIGURES

Figure 1: Sampling stations at Burford Bank, Dublin Bay, November 2007 (Image ©

Google Earth 2007)............................................................................................... 4

Figure 2: Principal components analysis (PCA) ordination of sediment data from

Burford Bank, November 2007. Data are standardised and collinear variables have

been removed. See Appendix I. ............................................................................ 7

Figure 3: Multidimensional scaling plot of replicate and mean macrofaunal data

from Burford Bank, November 2007. ...................................................................13

TABLE OF TABLES Table 1a: Results of one way Simper Analyses for species characterizing sites N1 to

N6 at Burford Bank, Dublin Bay, November 2007. ................................................10

Table 1b: Results of one way Simper Analyses for species characterizing sites B1 to

B8 at Burford Bank, Dublin Bay, November

2007……………………………………………………11

Table 2: Results of one-way ANOSIM test for significant differences between

stations. ..............................................................................................................14

Table 3: Results of BioEnv test to link macrofaunal distributions to sediment data.

...........................................................................................................................15

Table 4: Mean and standard deviations of univariate diversity indices from Burford

Bank Grab Fauna, Dublin Bay, November 2007. S=number of species, N= number of

individuals, H’(loge)= Shannon-Wiener diversity index, 1-lambda= Simpson’s index.

For further analysis see Appendix II. ....................................................................16

Table 5: Water Framework Directive Habitat Classification using the Infaunal

Quality Index (IQI). All stations are classified as having high status. .....................17

Table 6: Mean Sediment Profile Imagery results for Burford Bank, Dublin Bay,

November 2007. Penet is the mean prism penetration, aRPD is the mean apparent

redox potential discontinuity, BHQ Stage is the successional stage assigned by the

Benthic Habitat Quality Index, BHQ is the mean BHQ score, OSI is mean Organism

Sediment Index score, WFD BHQ is the Water Framework Directive classification

assigned by BHQ analysis.....................................................................................18

M07D01 Burford Bank Sediment Assessment November 2007

Moore Marine Services & Atlantic Resource Management Systems Page 3

1. Introduction This study was undertaken when Jacobs Engineering commissioned Moore Marine and Atlantic RMS

to carry out a baseline benthic ecological assessment of the dredge spoil disposal site at the Burford

Bank, Dublin Bay. Dublin Port Authority was planning to dredge the maintained navigation channel

into the Port and dispose of the spoil at the Burford Bank. The baseline assessment was carried out

before the dredging operation started. The Burford Bank site had been used in the past by Dublin

Port Authority for similar purposes, and is also used by several other smaller authorities for the ad

hoc disposal of small amounts of spoil material. The purpose of this assessment was to verify that the

site had recovered from previous disposal activities and was fit to receive the current planned spoil

disposal.

1.1 Methods

Sampling was carried out over three days in November 2007. The sample stations are shown in Figure

1. B7 and B8 are control stations that should have had no impact from any previous dumping

activities.

1.2 Sediments

Sediments were sampled at each station using a van Veen grab. Grain size distribution and organic

content were analysed using the methods outlined in Appendix I. The results of these analyses were

used to attempt to explain the distribution of the benthic communities by multivariate analyses, see

Appendix I and below for more details.

1.3 Macrofauna

Benthic macrofauna sample processing

Benthic macrofauna was sampled at each station using a van Veen grab. Five replicate grabs were

taken at each station. Samples were sieved at 1mm, fixed in 4% formaldehyde and later transferred

to 70% alcohol. All fauna was identified to species level where possible. Quality assurance was

provided by the Jacobs benthic lab in the UK who identified 20% of the samples. The remainder was

identified by Atlantic RMS using the most up to date keys available. Species nomenclature was

classified in accordance with Howson & Picton (1997).

Multivariate data analyses

The faunal data matrix was square root transformed and standardised and used to prepare a Bray-

Curtis similarity matrix using Primer 6. The similarity matrix was used to analyse the community

distribution using the following techniques (Clarke & Green 1988; Clarke & Ainsworth 1993; Clarke

1993; Clarke & Warwick 2001):



Figure 1: Sampling stations at Burford Bank, Dublin Bay, November 2007 (Image © Google Earth 2007)



Multi-dimensional scaling (MDS). This is an ordination technique that plots stations in two

dimensions to represent the similarity (usually the Bray Curtis similarity index is used for benthic data)

between stations. Stations that appear close together on the plot are similar to each other (Kruskal &

Wish 1978).

Anosim is an analysis involving permutative Mantel testing to investigate the significance of

differences between stations in similarity matrices. Its output is analogous to that of analysis of

variance. This technique is particularly useful for identifying station groups based on macrofaunal

distributions.

Simper is an exploratory data analysis that is useful for identifying species that characterise station

groups, and also the species that discriminate between groups. It does this by calculating and

comparing the contribution of the component species to within group similarity, and also to between

group dissimilarity. It is particularly useful for biotope classification when combined with ordination of

environmental data by principal components analysis (PCA).

Principal components analysis (PCA) is a standard parametric ordination technique that plots station

distributions in (usually) two dimensions based on linear combinations of variables. It is particularly

suited to analysis of the environmental variables in this study as there are relatively few variables (far

fewer than the likely number of species). Because of potential collinearity in these variables (for

example organic content being high correlated with grain size) some data reduction may be necessary

for this technique

BioEnv is a technique for pattern matching in environmental and biological similarity matrices. In a

similar manner to Anosim, BioEnv uses permutative Mantel testing to find the combination of

environmental variables that best explains the distribution of fauna. It is particularly useful for

explaining the change in biotopes using environmental data. BioEnv was be used to link the faunal

matrices produced in this project to the environmental parameters (Clarke & Ainsworth 1993).

Singular (i.e. unimetric) measures of community structure

The following commonly used summary measures were selected: density (N), species richness (S),

the Shannon–Wiener index (H’) and Simpson’s index. Density is expressed as individuals per

0.1m2

, and species richness as the number of species found at a station. The Shannon–Wiener (H’)

index (Shannon and Weaver, 1949) considers both species richness and the evenness component

of diversity. Simpson’s index is a more explicit measure of the latter, i.e. the proportional

numerical dominance of species in a sample (Simpson, 1949).

Combined (multimetric) univariate measures

For the AMBI index, benthic species are assigned to five ecological groups ranging from sensitive

to highly tolerant of stress. The complementary Biotic Coefficient is calculated according to the

percentage of each ecological group within a sample. Further details are described in Borja et al.

(2000). The index favoured by Ireland and the UK, IQI (Infaunal Quality index), used AMBI,



Simpson’s Index (as a measure of evenness in the apportioning of individuals among the species),

and the number of taxa as parameters. The individual measures have been weighted and

combined within the multimetric, in order to best describe the changes in the benthic

invertebrate community caused by anthropogenic pressure (ICES 2008).

IQI = (((0.38 × AMBIIQI

) + (0.08 × (1-λ’)IQI

) + (0.54 × SIQI

0.1)) − 0.4) ÷ 0.6

Where: AMBIIQI

is (1 − (AMBI BC ÷ 7)) ÷ (1 − (AMBI BC ÷ 7))MAX

(1-λ’)IQI

is (1 −λ’) ÷ (1 − λ’MAX

)

SIQI

is S ÷ SMAX

.

Each metric is normalized to a maximum value expected for that metric. MAX parameters relate

to the reference condition for that metric for a specific habitat which, in this case, is defined as

the maximum value observed in the dataset for this type of benthic assemblage during a benthic

intercalibration exercise. Currently (IQI version 4) the multimetric boundaries for water

framework directive classification of Eunis A5.2 and A5.3 marine sublittoral sands and muds are:

Bad = ≤ 0.24

Poor = 0.25 to 0.44

Moderate =0.45 to 0.64

Good = 0.65 to 0.74

High = ≥0.75.

1.4 Sediment Profile Imagery

Five replicate images were taken at each station using an Atlantic RMS DigiSPI camera (10.2 Mp). The

images were processed to produce indices of habitat quality and to investigate the distribution of

relict dredge spoil from previous dumping events. See Appendix III for full methodology and detailed

results.

2. Results

2.1 Sediments

Figure 2 shows the results of principal components analysis (PCA) on the sediment data. For full

details see Appendix I.



Figure 2: Principal components analysis (PCA) ordination of sediment data from Burford Bank, November 2007. Data are standardised and collinear variables have been removed. See Appendix I. In PCA, points that are closer together on the plot are more similar. Linear combinations of the

distributions of the sediment variables were used to create the plot. The equations for PC1 and PC2

are given in Appendix II. The essential message of Figure 2 is that B5, B4, B7 and B8 have relatively

high fine sand and very fine sand content (fs) while N6, N1, N2 and N3 are higher in very coarse sand

(vcs), medium sand (ms) and fine medium sand (fms). B1 is relatively high in silt clay (sc). The raw

sediment data (Appendix I) reveals that all the stations are essentially fine sands with mud. The B

stations tend to be finer that the N stations. All stations have low organic contents (all are <3% by Loss

on Ignition). This PCA ordination accounts for 72.4% of the variation in the sediment data.

2.2 Macrofauna

Simper is an exploratory technique for determining species that characterise communities. Results of

a station by station Simper analysis are shown in Table 1. The N stations are generally characterised

by the polychaetes Nephtys hombergii, Ophelia borealis and Spiophanes bombyx. They conform to the

JNCC habitat SS.SMu.ISaMu.NhomMac Nephtys hombergii and Macoma balthica in infralittoral sandy

mud and with Circalittoral muddy sands SS6 as defined by Fossit (2000). The B stations are generally

characterized by the bivalve Thracia phaseolina, the briitle star Amphiura filiformis and the

polychaete Lagis koreni. These stations correspond with Circalittoral muds SS7 as defined by Fossit



(2000) and the JNCC habitat SS.SMu.CSaMu.AfilMysAnit Amphiura filiformis, Mysella bidentata and

Abra nitida in circalittoral sandy mud. There is generally low similarity between replicates from the

same station, indicating that the sediment type is patchy on small scales or subject to frequent

perturbation.

Figure 3 shows the results of MDS analyses on the replicate and mean faunal data from each station.

The distribution of the faunal communities appears to match their spatial distribution reasonably

well. The stress value (goodness of fit) of the replicate plot is 0.18, indicating that fine scale pattern

may not be well represented. The mean plot has a good stress value at 0.03, and represents the

actual multivariate distribution of the fauna very well. Both ordinations show that B1, B4 and B5 are

similar to B7 and B8. The N samples are less similar to the B samples, N4 being the most similar to the

control sites.

Table 2 shows the results of ANOSIM analysis to determine significant difference between the

communities at the stations. Only stations not significantly different from one another are shown. For

full results see Appendix II. Almost all stations are significantly different from one another. Those that

do show some degree of overlap tend to be the stations closest together, indicating that the

community distribution is reflecting spatially variability, most likely in sediment type. This spatial

variability is probably indicative of physical stress due to shallow water depths, currents, tides and

passing heavy shipping.

Table 3 shows the results of BioEnv analysis to link the macrofaunal and sediment distributions. It

shows that the highest correlation possible (0.802) between the macrofaunal and sediment rank

distance matrices is derived from the distribution of fine medium sand. PCA analysis shows that the N

stations have relatively higher fine medium sand. The next highest possible correlation is 0.742 for

medium sand and fine medium sand, both of which are also associated with the N stations. There is

considerable power in the sediment data to explain the distribution of the macrofauna. The

macrofauna seems to be determined by small scale variation in grain size distribution caused by

physical stress.

Table 4 shows the mean and standard deviations for each univariate diversity index calculated for the

macrofaunal data. The replicate data are shown in Appenix II Table II.2. ANOVA results from analyses

using each diversity index in turn as the response and station as the factor are shown in Appendix II,

Table II.3. There are significant differences between the stations in the case of each diversity index.

Post hoc tests (Tukeys HSD) were performed to determine which stations were significantly different

and which were the same (Appendix II, Table II.4). The results of the post hoc tests indicate that there

are significant differences in terms of number of species (S), number of individuals (N) and Shannon-

Wiener diversity (H’) between N1, N2, N3 and N6 and the reference stations B7 and B8. In the case of

all of these indices the reference stations had a higher mean value indicating better habitat quality.

In general, coarser sediments have less diverse and less populace macrobenthic communitities. We

tested the hypothesis that the lower diversity values were due to differences in grain size distribution

by Spearman’s rank correlation analysis. Results (Table II.5, Appendix II) show that S, N and H’ are



highly significantly correlated with mean grain size, and the lower diversity at these stations may be

due to grain size distribution. This supports the results of the BioEnv analysis.

Table 5 shows the results of the Infaunal Quality Index (IQI) analyses for classifying the habitat quality

of each station for Water Framework Directive (WFD) purposes. All stations were classified as having

high ecological status indicating that the stations had largely recovered from previous dumping

events. IQI is largely based on AMBI. There is some recently published work indicating that,

particularly in shallow coastal water, AMBI may not be very sensitive in detecting disturbance effects

(Zettler et al 2007; Fleischer et al 2007; Ruellet and Dauvin 2007). This may reduce the power of IQI to

detect habitat quality changes, so the results of this analysis should be regarded with a certain degree

of caution. However, the results from this survey are not marginal, and indicate clearly that habitat

quality in the entire area is high.



Group N2 Average similarity: 36.29 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Nephtys hombergii 4.43 11.73 0.95 32.32 32.32 Ophelia borealis 4.09 10.82 1.14 29.81 62.13 Group N1 Average similarity: 26.75 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Ophelia borealis 4.32 10.44 0.79 39.02 39.02 Nephtys hombergii 3.19 5.83 0.89 21.8 60.82 Group N3 Average similarity: 21.12 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Nephtys hombergii 5.26 16.09 1.06 76.2 76.2 Group N4 Average similarity: 23.22 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Thracia phaseolina 3.52 7.08 2.29 30.48 30.48 Nephtys hombergii 2.21 4.3 1.11 18.51 48.99 Spiophanes bombyx 2.2 3.12 0.83 13.45 62.44 Group N6 Average similarity: 12.20 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Nephtys hombergii 1.62 2.28 0.61 18.69 18.69 NEMERTEA sp. 1.81 2.14 0.57 17.5 36.19 Spiophanes bombyx 1.67 1.71 0.62 14.06 50.25

Table 1a: Results of one way Simper Analyses for species characterizing sites N1 to N6 at Burford Bank, Dublin Bay, November 2007.



Group B7 Average similarity: 36.47 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Thracia phaseolina 2.71 4.32 3.23 11.86 11.86 Lagis koreni 2.51 3.27 1.09 8.97 20.82 Amphiura filiformis 2.62 3.08 1.11 8.46 29.28 Melinna palmata 1.79 2.5 2.59 6.84 36.13 Thyasira flexuosa 1.36 2.25 8.37 6.17 42.3 Phaxas pellucidus 1.31 2 2.43 5.49 47.79 Phloe inornata 1.31 1.99 1.15 5.45 53.24 Group B5 Average similarity: 27.31 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Sipuncula sp 3.11 5.45 1.08 19.96 19.96 Thracia phaseolina 1.84 2.48 1.08 9.07 29.04 Abra prismatica 1.81 2.37 0.81 8.68 37.72 Spiophanes bombyx 1.87 2.23 1.02 8.17 45.89 Spio decorata 1.57 1.94 1.01 7.12 53.01 Group B4 Average similarity: 39.00 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Amphiura filiformis 4.1 8.29 4.79 21.26 21.26 NEMERTEA sp. 2.21 4.07 1.87 10.42 31.68 Thracia phaseolina 2.84 3.6 1.08 9.24 40.92 Nucula nitidosa 1.7 2.88 2.78 7.39 48.31 Ampelisca brevicornis 1.92 2.68 1.12 6.86 55.17 Group B1 Average similarity: 23.63 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Sipuncula sp 3.07 4.8 1.12 20.3 20.3 Thracia phaseolina 2.39 2.96 1.07 12.54 32.84 NEMERTEA sp. 1.44 1.68 1.13 7.11 39.94 Amphiura filiformis 1.96 1.5 0.62 6.36 46.31 Lumbrineris gracilis 1.56 1.48 0.82 6.25 52.56 Group B8 Average similarity: 55.78 Species Av.Abund Av.Sim Sim/SD Contrib% Cum.% Amphiura filiformis 4.87 8.71 3.89 15.62 15.62 Thracia phaseolina 3.68 6.42 4.81 11.52 27.14 Lagis koreni 3.36 5.75 3.1 10.3 37.44 Nuculoma tenuis 1.91 3.23 2.24 5.79 43.22 Thyasira flexuosa 1.59 3.04 8.87 5.44 48.66 Abra alba 1.44 2.54 5.79 4.55 53.21

Table 1b: Results of one way Simper Analyses for species characterizing sites B1 to B8 at Burford Bank, Dublin Bay, November 2007.



Figure 3: Multidimensional scaling plot of replicate and mean macrofaunal data from Burford Bank, November 2007.



Global Test

Sample statistic (Global R): 0.5

Significance level of sample statistic: 0.1%

Number of permutations: 999 (Random sample from a large number)

Number of permuted statistics greater than or equal to Global R: 0

Pairwise Tests

R Significance Possible Actual

Number

>=

Groups Statistic Level % Permutations Permutations Observed

N2, N1 0.236 5.6 126 126 7

N2, N3 0.052 31.7 126 126 40

N1, N3 0.244 5.6 126 126 7

N3, N6 0.253 11.9 126 126 15

N4, N6 0.106 23 126 126 29

N4, B5 0.24 6.3 126 126 8

N4, B4 0.16 13.5 126 126 17

N4, B1 0.12 14.3 126 126 18

B7, B4 0.164 11.9 126 126 15

B7, B1 0.1 11.9 126 126 15

B7, B8 0.1 21.4 126 126 27

B5, B1 0.02 39.7 126 126 50

B4, B1 0.016 41.3 126 126 52

Table 2: Results of one-way ANOSIM test for significant differences between stations.

Pairwise tests show only stations that are not significantly different. For all tests see

Appendix II.

Table 2: Results of one-way ANOSIM test for significant differences between stations. Pairwise tests show only stations that are not significantly different. For all tests see Appendix II.



Global Test

Sample statistic (Rho): 0.802

Significance level of sample statistic: 1%

Number of permutations: 99 (Random sample)

Number of permuted statistics greater than or equal to Rho: 0

No.Vars Corr. Selections

1 0.802 5

2 0.746 4,5

3 0.727 4,5,13

2 0.721 5,13

4 0.647 3-5,13

4 0.622 1,4,5,13

2 0.598 3,5

3 0.592 3-5

3 0.591 3,5,13

3 0.579 1,4,5

Variables 1 = vcs, 3 = mcs, 4 = ms, 5 = fms, 6 = fs, 8 = sc, 11 = Skw, 12 = Kur, 13 = OC

Table 3: Results of BioEnv test to link macrofaunal distributions to sediment data.



site S N H'(loge) 1-Lambda' Mean 28 86.8 2.807 0.933 B1 Std. Deviation 13.91 59.701 0.403 0.023 Mean 25 76.2 2.725 0.918 B4 Std. Deviation 10.536 50.366 0.385 0.043 Mean 26.2 62.8 2.837 0.952 B5 Std. Deviation 18.674 59.931 0.536 0.018 Mean 34.4 179.8 2.942 0.924 B7 Std. Deviation 14.223 118.723 0.269 0.019 Mean 36 253.8 2.641 0.868 B8 Std. Deviation 11.937 119.778 0.267 0.047 Mean 8.6 25.8 1.678 0.784 N1 Std. Deviation 5.079 13.971 0.616 0.158 Mean 6.4 21.4 1.537 0.770 N2 Std. Deviation 1.949 10.991 0.407 0.142 Mean 7.4 25.4 1.353 0.662 N3 Std. Deviation 4.506 16.637 0.477 0.188 Mean 22.2 62.6 2.529 0.915 N4 Std. Deviation 14.533 63.752 0.389 0.022 Mean 14.5 33.75 2.231 0.885 N6 Std. Deviation 10.472 30.412 0.738 0.117

Table 4: Mean and standard deviations of univariate diversity indices from Burford Bank Grab Fauna, Dublin Bay, November 2007. S=number of species, N= number of individuals, H’(loge)= Shannon-Wiener diversity index, 1-lambda= Simpson’s index. For further analysis see Appendix II.



Stations AMBI Diversity Richness IQI WFD Class

B1 1.24 5.45 89 0.94 High

B4 1.32 5.05 69 0.91 High

B5 1.25 5.75 86 0.94 High

B7 1.44 5.41 95 0.92 High

B8 1.59 4.38 88 0.90 High

N1 0.71 3.68 27 0.88 High

N2 0.95 2.86 18 0.83 High

N3 0.99 3.53 27 0.86 High

N4 1.15 5.27 82 0.94 High

N6 1.20 4.97 46 0.89 High

Table 5: Water Framework Directive Habitat Classification using the Infaunal Quality Index (IQI). All stations are classified as having high status.

2.3 Sediment Profile Imagery

Functioning as an inverted periscope the sediment profile camera (Rhoads & Cande 1971) slices

vertically through the surficial sediments to take an undisturbed photograph of the sediment profile

(Figure 1). The oxidation state of the surficial sediments and the visible artefacts of bioturbation are

used to calculate indices of benthic habitat quality (Rhoads & Germano 1982, 1986; Nilsson &

Rosenberg 1997), see Appendix III. In this study we also investigated whether there was visible dredge

spoil in the images, and used the system of Rosenberg et al (2004) to classify the sites in terms of

Water framework Directive criteria. Five replicate images were taken at each site, but only good

quality images with sufficient penetration (>4cm) were analysed. Replicate results are shown in

Appendix III, as are representative images from the sites. Summary mean data are shown in Table 6.

Penet

(cm)

aRPD

(cm)

BHQ

Stage BHQ OSI

WFD

BHQ Dredge Spoil

B1 2.7 2.7 2.0 3.0 7.0 poor Oxidised

B4 6.2 4.1 2.0 5.0 9.0 moderate Oxidised

B5 5.9 4.0 2.0 6.0 8.3 moderate Oxidised

B7 4.5 2.8 2.0 6.0 7.3 moderate No

B8 5.4 2.0 2.0 7.0 6.0 Good No

N1 5.1 5.0 1.5 4.5 8.5 moderate No

N2 8.7 5.3 2.0 5.0 9.0 moderate No

N3 3.4 3.4 2.0 3.8 7.6 poor No

N4 7.1 2.1 1.3 2.8 3.8 poor Reduced



N6 4.7 3.4 2.0 5.0 8.0 moderate Oxidised

Table 6: Mean Sediment Profile Imagery results for Burford Bank, Dublin Bay, November 2007. Penet is the mean prism penetration, aRPD is the mean apparent redox potential discontinuity, BHQ Stage is the successional stage assigned by the Benthic Habitat Quality Index, BHQ is the mean BHQ score, OSI is mean Organism Sediment Index score, WFD BHQ is the Water Framework Directive classification assigned by BHQ analysis.

The stations at the edge of the study area, including the control stations, showed no sign of dredge

spoil. B1, B4, B5 and N6 showed signs of spoil from previous dumping, but this spoil was oxidised and

the sites had recovered well. N4 showed signs of both reduced and oxidised dredge spoil. It appeared

that there had been a small amount of recent disposal at the site.

All sites had a well developed mean aRPD (depth of oxidised sediment) at >2.5cm. The WFD

classification was Moderate for most stations, Poor for B1, N3 and N4 and Good for B8. The BHQ is

heavily dependent on seeing animal structures in the sediment profile for awarding good status to

sites. Because these sites are sandier than the fjordic environment where the BHQ was derived,

biogenic structures such as burrows and oxic voids may not as persistent in the sediment profile and

the BHQ may be underestimating habitat quality in this case.

The Organism-Sediment Index of Rhoads & Germano (1982, 1986) is less dependent on observing

particular features in the sediment profile than BHQ. In coastal waters OSI values of less than 6 are

usually taken as being indicative of disturbance. Only N4 is classified as disturbed using this measure.

This is due to the presence of fresh dredge spoil in some of the N4 images.



3. Overall Assessment 1. Sediment analyses reveal that the bottom types are typical of Irish coastal waters, being

muds and muddy fine sand of low organic content. The habitats conform well to those of

Fossitt 2000.

2. The macrobenthic communities are typical of these habitat types and their distribution

appears to be accounted for by changes in grain size distribution in the area using both

multivariate and univariate methods.

3. Habitat quality assessment using the Infaunal Quality Index (IQI) indicated that all of the

stations had high ecological status with reference to water framework directive criteria.

4. Sediment profile Imagery (SPI) analyses revealed that all stations had well oxidized surficial

layers and where dredge spoil was present it was generally well oxidized indicating recovery

from previous dumping events. There was some evidence of recent small scale dumping at

station N4.

5. WFD classification of the SPI data indicated that ecological status was poor to moderate, but

this method of classification is probably over dependent on observing fauna and burrows in

the sediment profile, as it was derived for very cohesive scandanavian fjordic sediments. An

alternative index the OSI indicated that only station N4 was disturbed.

6. Overall, the dumping site appears to have recovered well from previous dredging events and

to be capable of receiving further spoil.

7. The clear mitigation measure necessary is to ensure that any spoil disposed of at the site is

spread evenly around the disposal area, and that no particular area receives too large a

volume of spoil for water currents and macrobenthos to oxidize.



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Appendix I: Sediment analyses and results

Sediment Grain Size Distribution

The particle sizing samples taken from the core slices were frozen (<-20 °C) in zip-lock bags

prior to laser diffraction particle size analysis. Samples were defrosted for 24 hours before analysis to

ensure that no frozen aggregates would distort the particle size distribution frequencies. Samples

were analysed using a Malvern Mastersizer X, capable of sizing in the range of 1-2,000µm. The

samples were sonicated and stirred in distilled water while being analysed. Two replicate runs of

1,000 sweeps of the samples were performed. The distribution of apparent diameters encountered by

the laser in a sample sweep was used to calculate an equivalent sphere diameter particle size

frequency distribution (in which the particles are represented as spheres with the same distribution of

diameters as those measured by the laser). A cumulative frequency plot of the particle size

distributions was constructed from a table of particle size distributions equivalent to a series of

standard sieves.

Sediment descriptive statistics

The 5, 16, 50, 84 and 95 volume percentiles were calculated from the cumulative frequency plots.

These percentiles are used to calculate the following summary statistics developed by Folk (1974) for

the surficial sediment samples.

1) Graphic Mean (Mz)

Mz =(ø16 + ø50 + ø84)

3

where ø = -log2 of particle diameter (mm) of the respective percentiles.

Mz is a measure of the average particle size of the sediment, given in ø units. Negative values

correspond to coarse sand and gravel, while positive values correspond to medium to very fine sands,

clay and silt (see Table I.1).

2) Inclusive graphic standard deviation or Sorting (δi)

δi = ø84 - ø16

4 +

ø95- ø56.6

δi defines the degree of scatter of particle sizes about Mz. A theoretical perfectly

homogeneous sediment would have a δi of 0. Values <1 imply a homogeneous, well sorted sediment,

while those >1 imply a poorly sorted sediment (see Table I.1)

3) Inclusive graphic skewness (Ski)



Sk i = (ø16 + ø84 - 2(ø50))

2(ø84 - ø16)+

(ø5 + ø95 - 2(ø50))2(ø95- ø5)

Sk i characterises the asymmetry of the cumulative frequency curve. A range of values is

obtained from -1.0, strongly coarse skewed, to 1.0, strongly fine skewed.

Parameter Range of Values Implications Graphic mean (Mz) <-1.0ø Gravel

-1.0 to 0.0ø Coarse sand 0.0 to 3.5ø Sand >3.5ø Silt/clay

Sorting (δi ) <0.35 Very well sorted

0.35 to 0.5 Well sorted 0.5 to 1.0 Moderately sorted 1.0 to 2.0 Poorly sorted 2.0 to 4.0 Very poorly sorted

Skewness (Ski) -1.0 to –0.3 Very coarse skewed -0.3 to –0.1 Coarse skewed -0.1 to 0.1 Nearly symmetrical 0.1 to 0.3 Fine skewed 0.3 to 1 Very fine skewed

Range of particle size Classification

<63µm Silt/clay 63-125µm Very fine sand 125-250µm Fine sand 250-500µm Medium fine sand 500-710µm 710-1000 µm 1000-1400 µm

Medium sand Medium coarse sand Coarse sand

1,400-2,000µm Very coarse sand

Table I.1: The range of values and implications of the sediment particle cumulative frequency

parameters and the classification of sediment particle size ranges into size classes (after Buchannan,

1984).

Sediment Organic content

Sediment samples were dried to a constant weight at 105°C to determine dry weight.

Following this, samples were heated in an oven to 550°C for 4 hours, and reweighed. While many

temperature combinations have been used by various operators, this method produces the most

reproducible results (Heiri et al 2001). The loss on ignition was taken as the labile organic content of

the sediment. The returns of this technique include some organic nitrate and carbonate, as do all



other standard techniques including chromic acid oxidation (CAOV). For this reason it is better to refer

to this variable as organic content rather than organic carbon. Results are shown in Table I.2.

Sediments principal components analysis

The sediment data were used to make a Pearson’s correlation matrix to check for collinearity

between the sediment variables (Table I.3). Variables that were correlated to other variables in the

data set at 0.8 or above were removed (Mz and cs). The remaining variables were standardised and

normalised, and used in a correlation based principal components analysis to identify the parameters

that account for a large proportion of the variance in the original data set. The variances of the

principal components (Eigen values), the proportion and cumulative proportion of the total variance

explained by each principal component, and the coefficients for each principal component (Eigen

vectors) were calculated. A two-dimensional PCA ordination of the particle size classes and organic

content distributions was made (Figure 2).

(PCA) Principal Component Analysis Eigenvalues PC Eigenvalues %Variation Cum.%Variation 1 4.77 43.4 43.4 2 3.19 29 72.4 Eigenvectors (Coefficients in the linear combinations of variables making up PC's)

Variables PC1 PC2 vcs 0.037 0.308 mcs 0.156 -0.253 ms 0.368 0.101 fms 0.383 0.254 fs -0.358 0.331 vfs -0.44 -0.02 sc -0.044 -0.537 Srt 0.146 0.465 Skw 0.224 -0.336 Kur -0.398 0.151 OC -0.371 -0.127

Table I.3: Principal componenets Analysis (PCA) results for Burford Bank sediments, November 2007. This ordination accounts for 72.4% of the variation in the sediment data. See also Figure 2.



Sample d(v,5) d(v,10) d(v,16) d(v,25) d(v,50) d(v,75) d(v,84) d(v,90) d(v,95) Mz Sort Skew Kurtosis

B4 8.91 11.06 13.95 55.82 178.16 311.28 427.88 570.52 806.64 3.29 -2.22 -0.41 1.07 B1 7.06 7.97 8.76 9.83 12.88 50.35 161.54 826.19 1102.94 5.25 -2.16 0.75 1.27 B5 12.67 38.91 101.57 130.56 190.21 274.75 342.11 446.90 692.01 2.41 -1.31 -0.19 2.20 B7 17.07 97.58 118.01 134.41 166.11 200.59 221.25 244.23 309.12 2.62 -0.86 -0.33 2.96 B8 14.10 86.73 109.67 128.71 163.02 201.46 225.91 255.60 839.72 2.65 -1.15 -0.15 3.74 N1 167.95 195.82 218.72 245.05 313.75 416.18 486.26 578.98 839.05 1.64 -0.64 0.16 1.24 N2 186.82 212.33 236.39 267.87 358.08 504.34 608.83 739.29 1036.50 1.43 -0.72 0.18 1.11 N3 185.56 204.25 221.92 245.97 301.14 374.33 415.71 456.79 512.02 1.72 -0.45 0.04 0.99 N4 32.42 119.24 140.82 162.43 216.05 300.42 364.71 454.69 654.24 2.16 -1.00 -0.08 2.00 N6 120.58 148.39 167.54 189.76 242.01 316.38 366.01 427.71 607.79 2.02 -0.64 0.10 1.30

Sample vcs cs mcs ms fms fs vfs sc LOI%

B4 1.42 1.70 3.42 5.99 20.79 32.24 9.09 25.34 1.54 B1 0.00 7.44 3.44 1.13 1.26 4.99 4.58 77.16 0.78 B5 0.18 1.57 2.99 3.72 21.87 46.69 11.86 11.12 0.69 B7 2.92 1.15 0.33 0.00 4.57 71.70 12.26 7.08 1.55 B8 1.55 2.33 1.74 0.14 5.03 66.36 14.21 8.63 0.97 N1 2.81 1.51 1.89 8.57 58.41 25.03 0.00 1.77 1.69 N2 2.98 2.34 5.69 14.47 54.73 19.78 0.00 0.00 1.47 N3 0.00 0.00 0.06 5.77 67.47 26.69 0.00 0.00 2.48 N4 1.41 0.76 2.08 4.06 28.99 51.29 5.84 5.57 1.81 N6 1.29 1.99 1.29 2.14 39.79 47.91 2.33 3.26 2.08

Table I.2: Sediment grain size and organic content analyses results for Burford Bank sediments, November 2007. d(v,5) is the 5% volume percentile, i.e. 5% of the mass of the sample is smaller than this diameter in microns. Mz is graphic mean, Sort is Sorting, Skew is skewness. vcs is very coarse sand, fs is fine sand etc . See Table I.1 for explanation of these terms. LOI% is the percentage Loss On Ignition and represents the organic content of the sediment.



Appendix II: Macrofaunal data analyses results. Global ANOSIM Test for differences between stations Sample statistic (Global R): 0.5; Significance level of sample statistic: 0.1% Number of permutations: 999 (Random sample from a large number) Number of permuted statistics greater than or equal to Global R: 0 Pairwise Tests

R Significance Possible Actual Number >=

Groups Statistic Level % Permutations Permutations Observed

N2, N1 0.236 5.6 126 126 7

N2, N3 0.052 31.7 126 126 40

N2, N4 0.668 0.8 126 126 1

N2, N6 0.475 0.8 126 126 1

N2, B7 1 0.8 126 126 1

N2, B5 0.968 0.8 126 126 1

N2, B4 0.996 0.8 126 126 1

N2, B1 0.992 0.8 126 126 1

N2, B8 1 0.8 126 126 1

N1, N3 0.244 5.6 126 126 7

N1, N4 0.572 0.8 126 126 1

N1, N6 0.469 0.8 126 126 1

N1, B7 0.948 0.8 126 126 1

N1, B5 0.768 0.8 126 126 1

N1, B4 0.868 0.8 126 126 1

N1, B1 0.844 0.8 126 126 1

N1, B8 0.96 0.8 126 126 1

N3, N4 0.342 4.8 126 126 6

N3, N6 0.253 11.9 126 126 15

N3, B7 0.802 0.8 126 126 1

N3, B5 0.634 0.8 126 126 1

N3, B4 0.784 0.8 126 126 1

N3, B1 0.674 1.6 126 126 2

N3, B8 0.782 0.8 126 126 1

N4, N6 0.106 23 126 126 29

N4, B7 0.252 4.8 126 126 6

N4, B5 0.24 6.3 126 126 8

N4, B4 0.16 13.5 126 126 17

N4, B1 0.12 14.3 126 126 18

N4, B8 0.336 1.6 126 126 2

N6, B7 0.706 0.8 126 126 1

N6, B5 0.363 4 126 126 5

N6, B4 0.594 0.8 126 126 1

N6, B1 0.4 3.2 126 126 4

N6, B8 0.706 0.8 126 126 1

B7, B5 0.392 1.6 126 126 2

B7, B4 0.164 11.9 126 126 15

B7, B1 0.1 11.9 126 126 15

B7, B8 0.1 21.4 126 126 27

B5, B4 0.244 1.6 126 126 2

B5, B1 0.02 39.7 126 126 50

B5, B8 0.528 0.8 126 126 1

B4, B1 0.016 41.3 126 126 52

B4, B8 0.312 2.4 126 126 3

B1, B8 0.2 3.2 126 126 4

Table II.1: Results of one-way ANOSIM test for significant differences between stations at Burford Bank, Dublin Bay, November 2007.



S N 1-Lambda' H'(loge)

B11 47 173 0.93 3.35

B12 26 55 0.91 2.84

B13 22 87 0.92 2.66

B14 35 106 0.96 2.94

B15 10 13 0.95 2.25

B41 14 36 0.86 2.17

B42 17 25 0.96 2.71

B43 22 68 0.89 2.58

B44 34 105 0.94 3.06

B45 38 147 0.94 3.11

B51 29 66 0.96 3.12

B52 21 43 0.95 2.82

B53 57 164 0.97 3.58

B54 10 12 0.92 2.25

B55 14 29 0.96 2.41

B71 36 214 0.94 3.09

B72 58 373 0.90 3.29

B73 23 104 0.94 2.57

B74 24 86 0.91 2.86

B75 31 122 0.94 2.89

B81 31 230 0.80 2.37

B82 34 283 0.86 2.57

B83 28 107 0.91 2.82

B84 57 435 0.85 3.01

B85 30 214 0.91 2.44

N11 4 28 0.64 1.08

N12 8 38 0.59 1.30

N13 4 6 0.87 1.33

N14 11 18 0.93 2.25

N15 16 39 0.90 2.44

N21 7 9 0.92 1.83

N22 5 37 0.66 1.26

N23 9 25 0.85 1.90

N24 4 23 0.58 0.96

N25 7 13 0.85 1.73

N31 3 16 0.34 0.60

N32 5 12 0.76 1.35

N33 5 12 0.79 1.42

N34 14 45 0.78 1.93

N35 10 42 0.64 1.46

N41 45 173 0.90 2.96

N42 12 36 0.90 2.23

N43 26 56 0.90 2.72

N44 8 11 0.95 2.02

N45 20 37 0.93 2.71

N61 4 17 0.71 1.21

N62 29 79 0.94 2.96

N63 13 24 0.91 2.33

N64 12 15 0.97 2.43

Table II.2 Univariate diversity indices for replicate grab samples from Burford Bank, Dublin Bay

November 2007



Sum of Squares df Mean Square F Sig. Between Groups 5418.600 9 602.067 4.376 .001 Within Groups 5365.400 39 137.574

S

Total 10784.000 48 Between Groups 258727.544 9 28747.505 6.446 .000 Within Groups 173941.150 39 4460.029

N

Total 432668.694 48 Between Groups 15.838 9 1.760 8.273 .000 Within Groups 8.296 39 .213

H'(loge)

Total 24.134 48 Between Groups .390 9 .043 4.374 .001 Within Groups .386 39 .010

1-Lambda'

Total .776 48

Table II.3 One way ANOVA results from analyses using Univariate diversity indices as the response

and station as the factor. All results are highly significant.



S Subset for alpha = .05

SiteNum N 2 1 N2 5 6.40 N3 5 7.40 N1 5 8.60 N6 4 14.50 14.50 N4 5 22.20 22.20 B4 5 25.00 25.00 B5 5 26.20 26.20 B1 5 28.00 28.00 B7 5 34.40 B8 5 36.00 Sig. .147 .151

N

Subset for alpha = .05 SiteNum N 2 3 1

N2 5 21.40 N3 5 25.40 N1 5 25.80 N6 4 33.75 N4 5 62.60 62.60 B5 5 62.80 62.80 B4 5 76.20 76.20 B1 5 86.80 86.80 B7 5 179.80 179.80 B8 5 253.80 Sig. .872 .193 .772

Table II.4a Post hoc Tukey’s testing of One way ANOVA results from analyses using number of

species (S) and number of individuals (N) as the response and station as the factor. See also Table

II.3. The table shows homogeneous subsets of the stations.



H'(loge)

Subset for alpha = .05 SiteNum N 2 3 1

N3 5 1.35 N2 5 1.54 N1 5 1.68 1.68 N6 4 2.23 2.23 2.23 N4 5 2.53 2.53 B8 5 2.64 2.64 B4 5 2.73 B1 5 2.81 B5 5 2.84 B7 5 2.94 Sig. .121 .062 .349

. 1-Lambda'

Subset for alpha = .05 SiteNum N 2 1

N3 5 .66 N2 5 .77 .77 N1 5 .78 .78 B8 5 .87 .87 N6 4 .88 N4 5 .91 B4 5 .92 B7 5 .92 B1 5 .93 B5 5 .95 Sig. .066 .153

Table II.4b Post hoc Tukey’s testing of One way ANOVA results from analyses using Shannon-

Wiener diversity (H’(loge)) and Simpson’s Index (1-Lambda) as the response and station as the

factor. See also Table II.3. The table shows homogeneous subsets of the stations.



Correlations S N J' H'(loge) 1-Lambda' Mz LOI%

Correlation Coefficient 1.000 .988(**) .248 .855(**) .685(*) .830(**) -.467 Sig. (2-tailed) . .000 .489 .002 .029 .003 .174

S

N 10 10 10 10 10 10 10 Correlation Coefficient .988(**) 1.000 .200 .830(**) .648(*) .879(**) -.455 Sig. (2-tailed) .000 . .580 .003 .043 .001 .187

N

N 10 10 10 10 10 10 10 Correlation Coefficient .855(**) .830(**) .552 1.000 .927(**) .770(**) -.564 Sig. (2-tailed) .002 .003 .098 . .000 .009 .090

H'(loge)

N 10 10 10 10 10 10 10 Correlation Coefficient .685(*) .648(*) .794(**) .927(**) 1.000 .697(*) -.503 Sig. (2-tailed) .029 .043 .006 .000 . .025 .138

1-Lambda'

N 10 10 10 10 10 10 10 Correlation Coefficient .830(**) .879(**) .309 .770(**) .697(*) 1.000 -.503 Sig. (2-tailed) .003 .001 .385 .009 .025 . .138

Mz

N 10 10 10 10 10 10 10 Correlation Coefficient -.467 -.455 -.212 -.564 -.503 -.503 1.000 Sig. (2-tailed) .174 .187 .556 .090 .138 .138 .

Spearman's rho

LOI%

N 10 10 10 10 10 10 10 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

Table II.5: Spearman’s rank correlation matrix showing the results of correlation analyses between sediment graphic mean (Mz), sediment organic content (LOI%) and

univariate diversity indices. Number of species (S), number of individuals (N) and Shannon Wiener diversity (H’) showed highly significant positive correlation with Mz.,

i.e. the diversity increased as the sediments became finer.



Appendix III: SPI results and representative images

Sediment profile images were captured using an Atlantic RMS DigiSPI digital sediment profile

camera. Images were saved as 10.2 Mpixel 24 bit colour Raw files. Image analysis was performed

using Adobe Photoshop 9.0.2 and Image Analyst 9.0.3 (RVSI Europe Ltd., UK). Image Analyst

interrogates the image at the intensity level, or grey scale value, of individual pixel elements following

a scaling calibration. Grey scale analysis was used to determine the intensity level distribution within

the region of interest and to determine areas of similar grey scale shades. Connectivity analysis was

used to find groups of connected pixels with an intensity value above or below a user-defined

threshold value. Seventeen parameters were either measured or inferred directly from the images in

accordance with standard definitions and methods (Diaz & Schaffer 1988; Solan & Kennedy 2002). The

sediment-water interface was interpolated manually in all images.

The Organism Sediment Index (OSI; Rhoads & Germano 1982, 1986) was calculated using the

method outlined in Table III.2. The successional stage of each image (sensu Rhoads & Germano 1986)

was operator determined by comparing measured and inferred SPI parameters to the ecological

successional paradigm of Pearson & Rosenberg (1978). The Benthic Habitat Quality index (BHQ,

Nilsson & Rosenberg 1997) was calculated as shown in Table III.3. For both indices the lowest index

values are given to those bottoms that lack all surface and subsurface faunal structures and for which

the apparent redox potential discontinuity (aRPD) is either absent or very shallow. The presence of

gas bubbles in the sediment profile or other signs of hypoxia produce the lowest scores in the case of

the OSI. Similarly, the highest index values are assigned to those bottoms that show quantifiable

evidence of extensive infaunal reworking associated with a deep aRPD and the presence of burrows,

feeding voids and visble fauna in the images. The score range can vary from –10 to +11 for the OSI

and from 0 to 15 for the BHQ index.



Mean penet SBR aRPD Inf FPL Tubes Burr Stage BHQ OSI WFD BHQ

Dredge spoil

B1-4 2.70 0.03 2.7 0 0 0 0 2 3 7 poor oxidised

B4-2 6.21 0.04 4.95 0 0 0 0 2 5 9 moderate oxidised

B4-4 9.17 0.04 4.85 0 0 0 0 2 5 9 moderate Reduced

B4-6 3.08 0.04 2.55 1 0 1 0 2 5 9 moderate Oxidised


B5-2 3.19 0.10 2.64 0 0 1 0 2 4 7 poor Oxidised


B7-3 5.92 0.13 3.21 1 1 0 0 2 6 8 moderate No

B7-4 4.62 0.04 2.57 1 1 2 0 2 7 7 Good No

B7-5 2.84 0.09 2.6 0 0 2 0 2 5 7 moderate No

B8-2 7.30 0.08 3.49 0 1 1 2 2 8 8 good No

B8-5 3.45 0.04 0.6 0 1 2 2 2 6 4 moderate No

N1-4 6.95 0.09 6.9 0 0 0 0 1 5 9 moderate Oxidised

N1-5 3.18 0.18 3.18 0 0 0 0 2 4 8 poor Reduced

N2-1 9.48 0.10 4.91 0 0 0 0 2 5 9 moderate no

N2-2 6.43 0.11 5.5 0 0 0 0 2 5 9 moderate no

N2-3 5.03 0.04 4.91 0 0 0 0 2 5 9 moderate no

N2-4 9.30 0.06 5.45 0 0 0 0 2 5 9 moderate no

N2-5 13.35 0.12 5.61 0 0 0 0 2 5 9 moderate no

N3-1 2.36 0.11 2.33 0 0 0 0 2 3 7 poor no

N3-2 2.79 0.04 2.81 0 0 0 0 2 3 7 poor no

N3-3 6.15 0.10 5.93 0 0 0 0 2 5 9 moderate no

N3-4 3.72 0.19 3.92 0 0 0 0 2 5 9 moderate no

N3-5 2.11 0.05 2.1 0 0 0 0 2 3 6 poor no

N4-1 8.23 0.04 3.8 0 0 0 0 2 6 9 moderate no

N4-3 13.39 0.12 0 0 0 0 0 0 0 -4 Bad Reduced

N4-4 4.07 0.18 3.91 0 0 0 0 2 4 9 poor no

N4-5 2.72 0.04 0.65 0 0 0 0 1 1 1 poor no

N6-3 5.04 0.30 2.65 1 0 0 0 2 4 7 poor Oxidised

N6-5 4.27 0.16 4.18 1 0 0 0 2 6 9 moderate Oxidised

Table III.1: Replicate sediment profile imagery data from Burford Bank, Dublin Bay, November 2007.

Penet is the mean prism penetration, aRPD is the mean apparent redox potential discontinuity, Inf

is the presence of Infauna, FPL is the presence of a faecal pellet layer, Tubes is the presence of

surface tubes, Burr is the presence of burrows, Stage is the successional stage assigned by the

Benthic Habitat Quality Index, BHQ is the mean BHQ score, OSI is mean Organism Sediment Index

score, WFDBHQ is the Water Framework Directive classification assigned by BHQ analysis.



Chemical parameters Biological parameters

Mean aRPD depth (cm) Index value Successional stage

Index value

0 0 (Primary succession) >0 to 0.75 1 Azoic -4 0.76 to 1.50 2 Stage 1 1 1.51 to 2.25 3 Stage 1-2 2 2.26 to 3.00 4 Stage 2 3 3.01 to 3.75 5 Stage 2-3 4 >3.76 6 Stage 3 5 Methane present -2 (Secondary succession)

No/low dissolved oxygen -4 Stage 1 on 2 5

Stage 2 on 3 5

OSI = Sum of Chemical and biological index values, Range –10 to 11

Table III.2: Calculation of the Organism Sediment Index Rhoads & Germano,1982, 1986)

Surface structures Sub-score A Mean aRPD depth (cm) Sub-score C Faecal pellets 1 0 0 Tubes <2mm diam 1 >0.10 to 1 .0 1 Tubes >2mm diam 2 1.1 to 2.0 2 Feeding pit or mound 2 2.1 to 3.0 3 3.1 to 4.0 4 >4.0 5 Subsurface structures Sub-score B BHQ= Sum A+B+C Infauna 1 Bad 0 to 2 Burrows # 1-3 1 Poor >2 to 4 Burrows # >3 2 Moderate >4 to 6 Oxic voids <5cm Oxic voids >5cm

1 2

Good High

>6 to 8 >8 to 15

Table III.3: Calculation of Benthic Habitat Quality Index for Water Framework Directive applications

(Nilson & Rosenberg1997, Rosenberg et al 2004).



Representative Images Dublin Bay, November 2007.

N1 N2 N3

§ Clean sand, no trace of dredge spoil

B5 N6

• Oxidised fine sediments underlying dredge spoil. These sediments are recovering well from

previous disposal events.

• There is extensive recolonisation by benthic animals



N4 N4

• Very variable station

• Images taken within 2 minutes of each other

• The image on the left shows recent spoil disposal, perhaps on a small scale?

• The image on right is more typical of the station

B1 B4

• Hard ground with spoil overlying fine sand and clay. Large burrowing worm visible in B4

image.

• These stations have recovered following previous disposal events.



B7 B8

• Well mixed muddy fine sands

• These control stations show no impact from previous spoil disposal events

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