North Sea

Estuarine, Coastal and Shelf Science 129 (2013) 94e104

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Benthic habitat mapping of sorted bedforms using hydroacoustic and ground-truthing methods in a coastal area of the German Bight/North Sea Edith Markert*, Peter Holler, Ingrid Kröncke, Alexander Bartholomä Senckenberg am Meer, Department for Marine Research, Südstrand 40, D-26382 Wilhelmshaven, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 October 2012 Accepted 25 May 2013 Available online 11 June 2013

The continuously influence of human impacts on the seafloor and benthic habitats demands the knowledge of clearly defined habitats to assess recent conditions and to monitor future changes. In this study, a benthic habitat dominated by sorted bedforms was mapped in 2010 using biological, sedimentological and acoustic data. This approach reveals the first interdisciplinary analysis of macrofauna communities in sorted bedforms in the German Bight. The study area covered 4 km2, and was located ca. 3.5 km west of island of Sylt. Sorted bedforms formed as sinuous depressions with an east west orientation. Inside these depressions coarse sand covers the seafloor, while outside predominantly fine to medium sand was found. Based on the hydroacoustic data, two seafloor classes were identified. Acoustic class 1 was linked to coarse sand (type A) found inside these sorted bedforms, whereas acoustic class 2 was related to mainly fine to medium sands (type B). The two acoustic classes and sediment types corresponded with the macrofauna communities 1 and 2. The Aoinides paucibranchiata-Goniadella bobretzkii community on coarse sand and the Spiophanes bombyx e Magelona johnstonii community on fine sand. A transitional community 3 (Scoloplos armiger e Ophelia community), with species found in communities 1 and 2, could not be detected by hydroacoustic methods. This study showed the limits of the used acoustic methods, which were unable to detect insignificant differences in the fauna composition of sandy areas. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: macrofauna sediment type community structure hydroacoustic QTC island of Sylt

1. Introduction The seafloor and benthic habitats are continuously influenced by an increasing human input onto the seafloor during recent decades for e.g. sand extraction, oil exploration and production, installation of pipelines, underwater cables and windfarms, etc. In order to assess the recent condition of the different habitats and to address manmade and natural changes of seabed habitats, the large-scale mapping of these habitats is required. Hydroacoustic systems such as single-beam echosounder, sidescan sonar and multi-beam echosounder have been used during the last 20e30 years to map the seafloor and benthic habitats in a wide variety of submarine environments (Collins and Galloway, 1998; Ellingsen et al., 2002; Kenny et al., 2003; Freitas et al., 2003a, 2003b; Diaz et al., 2004; Bartholomä, 2006). In addition to

* Corresponding author. E-mail address: [email protected] (E. Markert). 0272-7714/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecss.2013.05.027

the pure depth information of the acoustic return signal of these systems, other properties such as backscatter information and waveform can be used for acoustic seabed classification (HughesClarke et al., 1996; Hamilton, 2005; Preston, 2001, 2009). The acoustic signals are influenced by a variety of geological seafloor properties such as sediment density, surface roughness, sedimentary structures, grain size of sediment (Collins and Galloway, 1998; Bornhold et al., 1999; Preston et al., 2004a) but also by benthic fauna such as blue mussel and oyster beds, shell debris (Quester Tangent Corporation, 2003; Wienberg and Bartholomä, 2005; Van Overmeeren et al., 2009), biogenic reefs of the tube-worm Lanice conchilega (Degraer et al., 2008), coral reefs (Gleason et al., 2006; Gleason, 2009) and seaweed (Preston, 2006; Hass and Bartsch, 2008). Freitas et al. (2003a, 2003b, 2011) showed that a specific benthic fauna is detectable by use of acoustic seafloor classification. Generally, acoustic techniques are regarded as an efficient, lowcost, easily repeatable remote sensing tool for mapping and monitoring the seafloor over large areas (Anderson et al., 2008; Van Rein et al., 2011). A satisfactory method for detecting biological

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species and communities using hydroacoustics will make it possible to classify and map marine habitats over large areas (Anderson et al., 2008). However, until now, the acoustic seabed classification can be used to monitor changes of the seafloor but not necessarily for all benthic biotopes. Since biological characteristics on the seafloor are not directly measurable using remote survey techniques, its success depends on the used acoustic system and the biological characteristics of the studied biotope (Van Rein et al., 2011). Here we show the limits of the acoustic techniques for detailed mapping of benthic habitats in a sandy area near the island of Sylt. The study area was chosen because of the occurrence of sorted bedforms of different dimensions. Sorted bedforms (Murray and Thieler, 2004) are spatially-grain-size-sorted features that are widespread on the shoreface and inner continental shelves all over the world (e.g. Garnaud et al., 2005; Bellec et al., 2010; Hallenbeck et al., 2012). In the study area they are oriented in more or less east-westerly direction (Bürk et al., 2011). The sorted bedforms showed a depth of 2e3 m compared to the neighbouring areas. Previous studies (Köster, 1971; Figge, 1981; Mielck, 2009) showed that these bedforms are typical not only for the working area, but for the north-easterly German Bight in general. Diesing et al. (2006) described the stability of sorted bedforms over a decadal scale near our study area. But until now, there has been no detailed information about the macrofauna communities in the sorted bedforms near the island Sylt. Generally, we expected similar macrofauna communities as in other sandy areas of the North Sea. Rachor and Nehmer (2003) described a Goniadella-Spisula community near the island Sylt, which typically inhabit the coarse sand and gravely areas. On the finer sand areas an Angulus fabula community (former Tellina fabula) was common (Rachor and Nehmer, 2003). The present study is part of the German framework project ‘Scientific monitoring concepts for the German Bight (WIMO)’. One goal of the WIMO project is to compare the results of different acoustic seafloor classification systems for subtidal areas and to determine how these results relate to the distribution of sediment properties and benthic macrofauna in the German Bight. The aim of this paper is to study: 1) the relationship between the surface sediment, the macrofauna communities and the hydroacoustic signals, and 2) to address the value of hydroacoustic methods for mapping sandy habitats. Fig. 1. Location of the study site ca. 3.5 km west of the island Sylt (a) and the 45 sampling stations (b).

2. Material and methods 2.1. Study site 2.2. Acoustic seafloor classification The study site was located approx. 3.5 km west of island of Sylt (Fig. 1a) in the German Bight. The size of the study site was approx. 2.5 km in the north-south direction, and approx. 1.6 km in the eastwest direction (approx. 4 km2). Water depth was 12e18 m, and generally increased from the southeast to the northwest (Fig. 1b). The study area is characterised by diurnal tides with a tidal range of 1.8 m at tide gauge List/Sylt (Bundesamt für Seeschifffahrt und Hydrographie, 2010). Bottom currents induced by the tides normally reach velocities of <0.25 m/s (Dick, 1986). However, during storm events, bottom currents can increase to >1.25 m/s (Kesper, 1992). Köster (1979) found, mainly northward bottom currents, parallel to the coast. During storm events the bottom current direction changes to normal to the coast. Storms from the north and northwest induce westward bottom currents, whereas south- to south-easterly storms cause eastward currents. In addition to tidal currents, residual currents may also affect the seafloor during storm events. Zeiler et al. (2000) pointed out that storm waves can reach a wave-length of 150 m and a height of 4e5 m.

Acoustic seafloor classification was carried out on data sets from three independent but simultaneously operated sonar systems: a Furuno FCV 295Ô single-beam echosounder, a Reson 8125Ô multibeam echosounder and Benthos 1624Ô sidescan sonar. The singlebeam system consisted of a Furuno FCV 295 echosounder and a pole-mounted Furuno 200 B e 8 B transducer. The working frequency of this system was 200 kHz. For this setting the beam angle was 7.4
at 6 dB. A pulse length of 0.5 ms, and an emission power of 1000 W were selected. The recorded depth range was limited to 50 m. The footprint of the system depends on water depth. For a water depth of 15 m (average water depth of the study area) a circular footprint covering 2.98 m2 was calculated. A Reson SeaBat 8125 system was used as a multibeam echosounder. The multibeam head was also pole-mounted on the portside of the vessel RV “Senckenberg” and this system emitted 240 beams. The beam-size was 1
along track, and 0.5
sideways. The opening angle of the system was 120
. The system operated at

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E. Markert et al. / Estuarine, Coastal and Shelf Science 129 (2013) 94e104

455 kHz. Total seafloor coverage of the Reson 8125 is ca. 3.5 water depth. The Benthos 1624 sidescan sonar system was also used. This system uses the advanced Chirp technology, allowing the use of the high frequency for wider ranges. The Benthos 1624 worked with two frequency ranges simultaneously. The high frequency signal was emitted at 370e390 kHz, whereas the low frequency signal was in the 110e130 kHz range. The low frequency (110 kHz) beam size was 0.5
(horizontal) and 55
(vertical). The high frequency (370e390 kHz) beam size was 0.5
(horizontal) and 35
(vertical). A range of 75 m was selected. The resulting swath-width was therefore 150 m. For the present study, the high frequency signals were further processed, because this frequency range was very close to the frequency of the Reson SeaBat 8125 system (455 kHz). The resolution of the system was 3.75 cm for the 400 kHz range.

For seafloor classification of single-beam echosounder data, a QTC 5.5Ô system was interfaced. The classification of sidescan sonar and multi-beam echosounder data was carried out using QTC SideviewÔ and QTC MultiviewÔ in the unsupervised mode respectively. One reason to select the QTC software packages was the experience gained with QTC View/QTC Impact at the Senckenberg Institut over the last decade (see Wienberg and Bartholomä, 2005; Bartholomä, 2006; Bartholomä et al., 2011). Another reason for using the QTC software was the almost identical statistical processing of data sets from single-beam echosounders on the one hand and multi-beam echosounders/sidescan sonar systems on the other hand. The classification of single-beam echosounder data was based on the analysis of the shape of the received echo, whereas for multibeam echosounder and sidescan sonar data classification was based

Table 1 Bulk contents of sand, gravel, mud and shell debris of the sediments (expressed as weight-percentage of bulk dry sample), classification based on sand, gravel and mud ratio after Folk (1954) and composition of the sand (expressed as weight-percentage of dry sand fraction). Sample Sand

Gravel

Mud

Shell debris Classification

0.063e2 mm >2 mm <0.063 mm ca % %

%

%

1 2 3 4 5 6 7 8

92.29 98.80 98.34 89.01 98.27 98.48 98.97 96.56

6.85 0.00 0.00 0.07 0.00 0.04 0.00 2.38

0.36 0.57 0.96 0.74 0.93 0.91 0.54 0.62

0.50 0.63 0.69 10.18 0.79 0.58 0.49 0.45

9

76.27

2.84

0.53

20.36

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

98.93 98.51 98.24 98.11 97.81 98.35 98.72 99.11 99.14 98.39 98.81 99.00 99.02 93.68 98.64 98.89 98.46 98.31 95.38

0.00 0.00 0.00 0.00 0.03 0.17 0.00 0.00 0.01 0.00 0.00 0.00 0.00 5.41 0.00 0.00 0.00 0.04 3.12

0.52 0.77 1.12 0.78 1.06 0.34 0.55 0.06 0.23 0.57 0.53 0.33 0.38 0.19 0.67 0.28 0.52 0.15 1.03

0.55 0.72 0.63 1.11 1.10 1.14 0.73 0.83 0.62 1.04 0.66 0.67 0.60 0.72 0.69 0.83 1.02 1.49 0.47

29 30 31

98.53 98.49 95.31

0.00 0.32 3.15

0.40 0.23 0.74

1.07 0.96 0.80

32 33 34 35 36 37 38 39 40 41 42 43 44 45

98.67 98.50 98.54 98.46 98.68 98.68 98.63 98.59 98.74 98.32 97.87 98.84 97.81 97.77

0.06 0.00 0.00 0.00 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.51 0.71 0.66 0.73 0.44 0.49 0.63 0.59 0.47 0.62 0.93 0.29 1.36 1.44

0.77 0.79 0.80 0.81 0.86 0.81 0.74 0.82 0.79 1.06 1.19 0.87 0.83 0.79

(Folk, 1954)

Very coarse sand Coarse sand

Medium sand

Fine sand

Very fine sand

(1)e(0) phi

(0)e(1) phi

(1)e(2) phi

(2)e(3) phi

(3)e(4) phi

1.00e2.00 mm

0.50e1.00 mm 0.25e0.50 mm 0.125e0.25 mm 0.063e0.125 mm

Gravelly Sand 16.13 Sand 0.08 Sand 0.03 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Slightly gravelly 1.97 Sand Slightly gravelly 2.19 Sand Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.08 Sand 0.00 Sand 0.00 Sand 0.09 Sand 0.00 Sand 0.00 Sand 0.14 Sand 0.00 Gravelly Sand 2.81 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Slightly gravelly 3.75 Sand Sand 0.00 Sand 0.63 Slightly gravelly 4.28 Sand Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.00 Sand 0.11

59.06 0.24 0.88 0.51 0.38 3.23 0.00 51.22

19.38 14.39 15.59 24.32 15.08 48.52 13.61 40.43

5.13 83.30 82.02 73.53 80.33 46.41 82.21 5.85

0.08 1.82 1.37 1.50 4.01 1.60 4.09 0.38

43.52

48.34

5.50

0.26

0.01 0.51 0.14 0.24 0.52 9.45 1.27 1.61 0.96 0.68 0.73 1.04 0.28 52.74 0.31 0.32 0.13 1.07 74.46

14.07 9.28 12.92 13.87 16.43 56.24 22.44 47.87 30.69 17.71 26.81 31.55 11.93 36.34 8.50 15.25 10.29 29.84 17.89

82.07 85.80 83.20 83.18 77.91 32.96 75.76 49.70 66.12 77.69 69.75 63.75 83.80 8.08 84.68 82.52 86.21 66.90 3.43

3.72 4.29 3.59 2.59 4.99 1.17 0.36 0.69 1.92 3.65 2.50 3.42 3.73 0.10 6.32 1.77 3.16 1.99 0.37

0.85 3.29 59.41

11.61 29.74 24.28

84.75 63.81 11.54

2.70 2.35 0.38

0.69 0.44 0.00 0.17 0.00 1.00 0.00 0.00 0.50 0.25 0.41 0.07 0.00 0.23

17.93 20.23 6.78 13.78 10.78 30.08 16.95 13.25 20.45 13.29 12.52 8.86 12.95 10.64

80.20 78.11 90.04 83.34 86.27 67.10 77.25 83.96 76.78 84.88 84.14 88.14 83.79 86.64

0.95 1.09 3.08 2.56 2.85 1.71 5.67 2.59 1.98 1.47 2.83 2.82 3.15 2.27

E. Markert et al. / Estuarine, Coastal and Shelf Science 129 (2013) 94e104

on backscatter images. Preston et al. (2003) pointed out, that extensive quality control is the essential first step for consistent quality classes. Further processing steps include the compensation for depth, range, and insonification angles. After this compensation, the time-series data from the single beam echosounder were aligned by their bottom picks and summed in stacks of 5. Special algorithms are then used for echo-shape analysis. These algorithms generated 166 so called “full feature vectors” (FFV’s). With images, borders of rectangular patches were distributed over the unmasked regions of backscatter images. The matrix of amplitudes in each patch was presented to the image feature algorithms. These algorithms generate 132 “full feature vectors” (Preston et al., 2004a). From now on, the further processing of the time-series and image data is exactly the same for all systems. Before Principal Component Analysis (PCA), the features from the entire survey area are merged to make a data set with wide sediment variability. PCA reduces the features to three components, Q1, Q2, and Q3 and only these components are used for clustering (Preston et al., 2004a). Preston et al. (2004b) pointed out that segmentation was done in the three-dimensional space of the Q values. Starting with all records in the same class, a variant of the k-means clustering method divided them into clusters, which became the acoustic classes. Each record, originating from a stack of single-beam echoes or from the amplitudes in a rectangle, was assigned to the closest class centre in Q space in a process that was iterative and stable. Each cluster represented an acoustically distinct area. 2.3. Sediment and macrofauna sampling Sediment and macrofauna sampling was carried out immediately after the acoustic survey during the same cruise. The positioning of the sampling sites for macrofauna and sediment samples was selected according to the acoustic information from a preliminary sidescan sonar mosaic generated onboard. To gain a detailed view of the area, we used defined transects for macrofauna sampling, to get a good coverage of the spatial variability in the study area. It would be less time-consuming and cheaper to sample spot samples based on

97

the results of the acoustic survey, but to get a first impression and information about the spatial variability in this area we instead decided to sample this closed-meshed grid. Samples were taken at 45 stations using a 0.1 m2 Van Veen grab based on 5 transects each of 9 stations (Fig. 1b). At each station three grabs were taken: two grabs for macrofauna and one grab for sediment analysis. Capperucci and Bartholomä (2012) described the problems of grab sampling accuracy due to strong currents. They found a positioning error of replicates with an average distance of 20 m (max. 32 m). 2.3.1. Macrofauna sample procedure Samples for macrofaunal analyses were sieved onboard over 1 mm mesh size, and the retained material was fixed with 4% buffered formaldehyde. Samples of coarse grained sediments were decanted before sieving. In the laboratory the samples were sieved again over 1 mm mesh size, organisms were stained with Rose Bengal and sorted. After sorting, the organisms were identified to species level. 2.3.2. Sediment sample procedure After a photographic documentation of the sediment surface, subsamples of the surface sediments (the upper 2 cm) were taken from the grabs for grain size analyses. In the laboratory the samples were placed into semi-permeable hoses and de-salted using fresh water for 24 h. The de-salted sample was washed through a 0.063 mm mesh screen to separate the mud fraction (<0.063 mm). The coarse fraction was then dried and dry-sieved over a 2 mm mesh screen. Shell debris and gravel were sorted and weighed in order to determine the gravel content (>2 mm) and the content of shell debris >2 mm. The sand size fraction (>0.063 mm to <2 mm) was weighed, treated with hydrochloric acid, and weighed again to determine the content of sand sized shell debris. For the total amount of shell debris of the samples (Table 1), the shell debris of the gravel size fraction and the sand size fraction were summed up. The sand fraction (>0.063 mm to <2 mm) was split into 1/4 F units (F ¼ log2 diameter of particle in mm) from 1 F to 4 F, using calculations from settling velocities measured by a MacroGranometerÔ settling tube (Flemming and Thum, 1978; Tucker,

Fig. 2. Hierarchical clustering of the sediments based on the composition of the sand fraction (diameter >0.063 mm to <2.0 mm). One cluster related to coarse (type A, station 1, 8, 9, 23, 28 and 31) and one related to the fine to medium sands (type B, all other stations). Correlation was used as similarity measure.

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E. Markert et al. / Estuarine, Coastal and Shelf Science 129 (2013) 94e104

1988). The results of the analyses of the sand fraction shown in Table 1 were rearranged according to the classes of Wentworth (1922), ranging from very coarse sand (1 F to 0 F; 1.0 mme 2.0 mm), coarse sand (0 F to 1 F; 0.500 mme1 mm), medium sand (1 F to 2 F; 0.250 mme0.500 mm), fine sand (2 F to 3 F; 0.125 mme0.250 mm) to very fine sand (3 F to 4 F; 0.063 mme 0.125 mm), 2.4. Data analysis The statistical analysis for macrofauna data used the software PRIMER v6 (Plymouth Marine Laboratory) (Clarke and Gorley, 2006). The data of the two macrofauna samples per station were averaged. Diversity (ShannoneWiener Index, Pielou’s evenness index) were analysed according to Shannon and Weaver (1949) and Pielou (1969). For multivariate analyses, the data were fourth root transformed and the BrayeCurtis similarity coefficient (Bray and Curtis, 1957) was calculated. Ordination was done by MDS (non-metric multidimensional scaling) (Shepard, 1962; Kruskal, 1964). For testing the significant differences between communities the ANOSIM randomisation test was used (Clarke and Green, 1988). SIMPER was used to detect the characteristic species per community (Clarke and Warwick, 2001). The abundances of the characteristic species were averaged per community such that abundance can <5. In order to verify the most probable amount of sediment classes gained by acoustic seafloor classification in the study area, the composition of the sand fraction (diameter >0.063 mm to <2.0 mm) was submitted to hierarchical cluster analysis (Clarke and Warwick, 2001) using the PAST software package (Hammer et al., 2001). Correlation was used as similarity measure. The final acoustic file was imported into a Geographic Information System

Fig. 4. MDS of the macrofauna communities 1, 2 and 3.

(ArcGIS 10) to produce maps of acoustic diversity and macrofauna communities. 3. Results 3.1. Sediments After Folk (1954) most samples of the working area were classified as sand although the sediments in the study area ranged from gravelly sand, slightly gravelly sand to sand. The mud content (<0.063 mm) was generally low (<1.5%). Only two samples (4: 10.18% and 9: 20.36%) were characterized by a higher amount of

Fig. 3. Grain size frequency curves of the sand fraction in ¼F steps and statistics for samples 1, 15, and 22. Sample 1 consists of coarse sand and is located in the sorted bedforms. Sample 15 and 22 are located outside the sorted bedforms and are classified as medium sand (15) and fine sand (22).

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Table 2 The mean taxa number, mean abundance, mean Shannon-Index and mean evenness and characteristic species (ind./m2) of the macrofauna communities 1, 2 and 3. The highest mean abundances per cluster are highlighted..

Cluster 1

2

3

2-7, 10-14, 16, 19, 20, 22, Sampling sites

1, 8, 9, 23

24-26, 28-30, 32-45

15, 17, 18, 21, 27, 31

Taxa/0.1m2

17.3

21.1

18.2

Mean abundance (N/m2)

2990

1156

1138

Mean Evenness (J'/ m2)

0.4

0.7

0.7

Mean Shannon (H'(loge)/ m2)

1

2.1

2.02

Aoindes paucibranchiata

2342.5

2.3

17.5

Goniadella bobretzkii

288.8

2

4.2

Ophelia spp. juv.

196.25

3.4

199.2

21.6

25

Dominant species

Ophiura spp. juv. Scoloplos armiger

6.3

160.9

345

Spio goniocephala

1.3

12

165.8

Echinocardium spp. juv.

6.3

79.9

76.7

Lanice conchilega

27.5

75.1

9.17

Lanice conchilega juv.

0

39

5.8

Magelona johnstoni

0

130.1

27.5

Nephtys hombergii

0

14.9

0

Spio martinensis

0

18

6.7

Spiophanes bombyx

2.5

400.3

137.5

Spisula eliptica juv.

11.3

65

42.5

Urothoe poseidonis

0

40.9

6.7

100

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shell debris (Table 1). The gravel content (>2 mm) varied between 6.85% and 0% (Table 1). The hierarchical clustering based on similarity of the sediment samples (Fig. 2) revealed two sediment types: Stations 1, 8, 9, 23, 28, and 31 were characterized by a higher amount of very coarse sand and coarse sand defined as sediment type A (Table 1); sediment type B is dominated by medium and fine sand. Grain size frequency curves of the sand fraction of samples 1, 15 and 22 (Fig. 3) verified the results of the cluster analyses. The phi moment mean of sample 1 (0.655) falls into the coarse sand class of Wentworth (1922), whereas sample 15 (phi moment mean 1.769) is characteristic for medium sand. The phi moment mean of sample 22 (2.429) classifies the sediment as fine sand. 3.2. Macrofauna In total, 78 macrofauna taxa were identified. 79% of organisms were annelids, less dominant were the crustaceans (3.9%), followed the molluscs (5.4%) and the echinoderms (7.2%). In contrast to the sediment analysis, the multidimensional scaling revealed three significant different macrofauna communities (Fig. 4), according to the ANOSIM test (Global R Value 0.89; significance level of sample statistic 0.1%). The three macrofauna communities had a few species in common, but abundance and species richness revealed differences between their characteristic species (Table 2), e.g. the abundance of the polychaete Spiophanes bombyx with a maximum of 400.3 ind/m2 in community 2 and a minimum of 2.5 ind/m2 in community 1. The mean Shannon-Index (H0 (log e)/ m2) and the mean evenness (J0 /m2) after Pielou (1969) increased from community 1 to 2, while values for community 2 and 3 were similar. The highest mean abundances of the dominant species per cluster are highlighted in Table 2. Our results showed a lower diversity and a higher taxa number in the coarse sand areas (community 1), in contrast to the fine sediment areas, where a higher diversity and a high abundances ware found (community 2 and 3).

The community of cluster 1 (Fig. 4, Table 2) was an Aoinides paucibranchiata-Goniadella bobretzkii community with high abundance of few species such as the polychaetes A. paucibranchiata, G. bobretzkii or juvenile Ophelia spp., which prefer coarser sediments (Hartmann-Schröder, 1996). Most of the stations in the study area belonged to community 2 (Fig. 4, Table 2). Dominant species were polychaetes such as Spiophanes bombyx, Magelona johnstonii, Lanice conchilega and Scoloplos armiger with a preference for fine or medium sands (Hartmann-Schröder, 1996). The community 3 (Fig. 4, Table 2) was a Scoloplos armiger- Ophelia community, dominated by the polychaetes Scoloplos armiger, Ophelia spp. juv., Spio goniocephala and S. bombyx. This community represented a transitional stage inhabiting species e although in lower abundances e of both cluster 1 and 2. In our special case, less sampling stations could have led to a loss of information about the transitional communities in the study area. Because of the first acoustic results we would have expected only two different communities instead of three. 3.3. Acoustic seafloor classification The seafloor of the study area was mapped simultaneously by three acoustic devices, which differ in resolution depending on frequency and beam geometry. The backscatter mosaic for the sidescan sonar data shows the distribution of sorted bedforms in the survey area (Fig. 5a). The sorted bedforms are characterized by a stronger backscattering (darker on Fig. 5a) compared to the surrounding areas (lighter on Fig. 5a). The sidescan sonar mosaic (Fig. 5a) also shows that no further structures are visible in the survey area. Close-ups of the sidescan sonar data (Fig. 5bed) demonstrated the sharp boundaries between sorted bedforms and the surrounding sediments. The sidescan sonar data did not show any transitional zone between the sorted bedforms and the surrounding seafloor. The classification of the backscatter data in the unsupervised mode by k-means clustering showed a minimum score for 2 seafloor classes. The sediment data also indicated that

Fig. 5. The backscatter mosaic (a) shows the location of sorted bedforms in the survey area. The close-ups (bed) are characterized by sharp boundaries without any transitional zones between sorted bedforms and surrounding sediments.

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the study area can be described by two main sediment types. Sediment type A consisted of coarse sand, whereas sediment type B was composed of fine to medium sand. Based on these results, we decided also to split the hydroacoustic data of the single-beam echosounder and multi-beam echosounder into two acoustic seafloor classes. For all three hydroacoustic systems these two acoustic classes showed a very clear distribution pattern (Fig. 6aec). Acoustic class 1 was found in the eastewest trending depressions, whereas acoustic class 2 was related to the seafloor outside of the depressions. Inside of nearly east-west trending sinuous depressions of the sorted bedforms predominantly coarse sand covered the seafloor (Fig. 7a). Outside of the depressions predominantly medium to fine sand is present. All three systems showed a similar distribution pattern of the two acoustic classes. The areapercentage of the two classes for the three systems was more or less identical (Fig. 6d). The single-beam echosounder data, processed using QTC ImpactÔ, yields 19% for acoustic class 1 and 81% for acoustic class 2, whereas for the sidescan sonar data (QTC SideviewÔ) 19.3% (class 1) and 80.7% (class 2) were determined. For multi-beam echsounder data (QTC MultiviewÔ) the areapercentages of the acoustic classes were 19.7% (class 1) and 80.3% (class 2). In contrast to the two acoustic classes, we found three macrofauna communities. According to the hydroacoustic map, the four stations of macrofauna community 1 were situated on the coarse sand depressions of the sorted bedforms (Fig. 7b). The stations of

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macrofauna community 2 and 3 were located in the fine to medium sand areas outside of the sinuous depressions (Fig. 7b). They were not separated by two seafloor classes, but the macrofauna community 2 dominated the study area. 4. Discussion This study focused on mapping a sandy area near the island of Sylt, using sediment analysis, macrofauna community analysis and acoustic measurement with an aim to show the value of a combination of acoustics with the biotic data. A comparison of the different used acoustic methods is not be the main focus here. In the study area, sorted bedforms with an east-west orientation (Bürk et al., 2011) structured the seafloor. These bedforms consisted of coarse sediments (type A) and were organized into elongate bands which are located in slight depressions on the seafloor. The acoustic data from the sidescan sonar (Fig. 5) showed an abrupt change of backscatter intensity associated with the sorted bedforms. This sharp boundary between the sorted bedforms and the surrounding sediments was also verified by Bürk et al. (2011), analyzing the backscatter data from a Kongsberg EM 710 multibeam echosounder in the survey area. Hydroacoustic surveys between 2007 and 2012 (Mielck, pers. communication) show that the location of sorted bedforms is stable in the long term. However, some dynamics were observed during surveys in two consecutive years (Mielck, pers. communication), indicating flow-directed

Fig. 6. Seafloor classification of single-beam echosounder data (a), seafloor classification of multibeam echosounder data (b), seafloor classification on sidescan sonar data (c). Spatial coverage of the two classes in the study area (d) for single-beam echosounder (Impact), sidescan sonar (Sideview), and multi-beam echosounder (Multiview).

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Fig. 7. a) The distribution of the two sediment types (coarse (type A) and fine to medium sand (type B)), and b) the distribution of the three macrofauna communities 1, 2, 3 superimposed on the acoustic diversity map (acoustic classes 1, 2).

habitats across broad-scales. In the present study, the hydroacoustic data show two main seafloor classes for the area near the island of Sylt. According to Diesing et al. (2006), in most instances the boundaries between fine sand and coarse sediment of sorted bedforms are sharp. These sharp sediment boundaries were also identified in this study. Automatic seafloor classification based on data from three independent hydroacoustic systems shows nearly identical results. However, image-based systems such as sidescan sonar and multibeam-echosounder provide a full spatial coverage of the seafloor, whereas data from the single-beam echosounder only gain a two-dimensional “strip-information”. Between the survey lines, no data exist and information has to be extrapolated. Therefore we prefer systems with a full seafloor coverage for habitat mapping. Acoustic seafloor class 1 was related to coarse sand areas (sediment type A) in the sorted bedforms and the macrofauna community 1. It has been shown that distinct echosounding classes correspond to distinct sediment type (Freitas et al., 2003a, 2005, 2006, 2008; Riegl et al., 2007). Freitas et al. (2011) found that acoustic systems were able to identify different areas based on sediment grain size, which were related to different benthic communities. Haghi et al. (2012) showed that in partially monotone sediment type with almost similar acoustic signatures the same macrofauna communities exist. In our study, the hydroacoustic signal for seafloor class 2 with fine to medium sands (sediment type B) covered macrofauna community 2, which dominated the study area. Community 3 could not be detected by a separated seafloor class and can be defined as a transitional community (Van Hoey et al., 2004) between acoustic seafloor class 1 and 2. The acoustic signal could not distinguished between macrofauna communities 2 and 3. Freitas et al. (2006) also described similar results for an area near the coastal shelf of Lisbon with three acoustic classes but four so-called biological affinity groups. In contrast to our study, they had difficulties to classify the coarse sand areas. In contrast to this, Freitas et al. (2003a) described a mismatch between the optimal acoustic splitting and the benthic community data in another area near the coastal shelf western Portugal, where the biological data revealed only two benthic communities in comparison to three acoustic classes. Van Rein et al. (2011) also found that physically defined biotopes can easily be distinguished by acoustic seabed classification, in contrast to biologically defined biotopes. Thus, acoustic methods seem to be unsuitable for detecting insignificant differences in sediment and thus fauna composition. So, this study shows the limits of the used acoustic methods. 5. Conclusions

movement of sediment across the seafloor. The restricted presence of community 1 taxa to the coarse sands in depressions and the preference of fine to medium sand taxa of community 2 and 3 for the rest of the study area became visible. The distribution of macrofauna communities is known to be highly correlated on small spatial scale with the sediment composition, which itself is influenced by the hydrodynamic energy (Salzwedel et al., 1985; Künitzer et al., 1992; Ellingsen et al., 2002; Rachor and Nehmer, 2003; Haghi et al., 2012), the primary production in the water column and the organic content of the sediment (Rhoads, 1974; Rhoads and Boyer, 1982; Snelgrove and Butman, 1994; Kröncke, 2006). The geomorphology and sediment texture in the study area were described based on ground-truthing data analysis and interpretation of acoustic data classification from single beam echosounder, sidescan sonar and multibeam echosounder data. Van Rein et al. (2011) demonstrated that acoustic seabed classification can be used as a tool for detecting spatial changes in marine

In general, hydroacoustic methods are suitable for identifying areas with significant differences in sediment grain size. However, our hydroacoustic approach was not able to identify areas characterized by a transitional macrofauna community. Thus, groundtruthing is essential for every acoustic survey to adequately verify the acoustic results and determine slight shifts in benthic communities (Van Rein et al., 2011). The combination of hydroacoustic techniques and ground-truthing is an easily repeatable and low cost seafloor monitoring method. The acoustic methods offer an opportunity of mapping benthic habitats on a large spatial scale instead of a process of inference around a matrix of spot samples (Brown et al., 2002). Acknowledgements This study is part of the project “Scientific monitoring concepts for the German Bight (WIMO)”, funded by the Lower Saxony Ministry for Environment and Climate Protection and the Lower Saxony

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Ministry for Science and Culture. We like to thank Captain Karl Baumann and the crew of RV ”Senckenberg” help with sampling and Corinna Schollenberger, Astrid Raschke and Henning Schröder for technical assistance. Finn Mielck (Alfred-Wegener-Institut für Polar- und Meeresforschung, Wattenmeerstation List, Sylt) provided additional information and unpublished data on sorted bedforms in the study area. References Anderson, J.T., Van Holliday, D., Kloser, R., Reid, D.G., Simard, Y., 2008. Acoustic seabed classification: current practice and future directions. ICES Journal of Marine Science 65, 1004e1011. Bartholomä, A., 2006. Acoustic bottom detection and seabed classification in the German Bight, southern North Sea. Geo-Marine Letters 26, 177e184. Bartholomä, A., Holler, P., Schrottke, K., Kubicki, A., 2011. Acoustic habitat mapping in the German Wadden Sea e comparison of hydroacoustic devices. Journal of Coastal Research 64, 1e5. Special Issue. 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