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Defining marine landscapes at a detailed level and their relevance in a biological context, experience from the Belgian continental shelf.

Author(s):

Kristien Schelfaut (UGent), Els Verfaillie (UGent), Vera Van Lancker (UGent)

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Kristien Schelfaut ([email protected]) Els Verfaillie ([email protected]) Vera Van Lancker ([email protected])

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Isabelle Du Four (UGent)

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4

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WE_UGent_MarineLandscapesBCS.pdf English

Number of pages:

30 (including this wrapper)

Summary:

The overall aim of the project was to use available geological, physical and hydrographical data, combined with available ecological information, to produce simple broad scale and ecologically relevant maps of the seabed features for the whole Belgian part of the North Sea. On the Belgian shelf (3600 km²), there is generally a large availability of various data including an extensive biological dataset for validation. The physical data (related to topography, sedimentology, and energy regime) have recently been compiled at a 250 m grid resolution and practically covers the whole shelf. At first, various geophysical datasets (bathymetry, sedimentology, absence/presence of bedforms, bed shear stress, absence/presence of grind and slopes) were combined and this led to a definition of 17 landscapes. These landscapes had a clear relation with the terrain and as such they are valuable for different purposes (e.g spatial planning related to aggregate extraction, for the prediction of seabird distribution). However, the correlation with the occurrence of

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macrobenthic communities was somewhat blurred and the amount of landscapes seemed unmanageable. Still, the work undertaken has the potential to underpin important aspects of sustainable development in the marine environment. Schelfaut, K., Verfaillie, E., Van Lancker, V., 2007. Defining marine landscapes at a detailed level and their relevance in a biological context, experience from the Belgian continental shelf. Worked example for the MESH final guidance, 27 pp. Marine landscapes, seabed, topography, environmental parameters, GIS, modelling, ecological validation.

Bookmarks: Related information:

Connor, D.W., Gilliland, P.M., Golding, N, Robinson, P., Todd, D., & Verling, E. 2007. UKSeaMap: the mapping of seabed and water column features of UK seas. Joint Nature Conservation Committee, Peterborough. Golding, N., Vincent, M.A., Connor, D.W., 2004. The Irish Sea pilot – Report on the development of a marine landscape classification for the Irish Sea. JNCC 30 pp. Van Hoey, G., Degraer, S., Vinx, M., 2004. Macrobenthic community structure of softbottom sediments at the Belgian continental shelf. Estuarine, Coastal and Shelf Science, 59, 599-613.

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Defining marine landscapes at a detailed level and their relevance in a biological context, experience from the Belgian Continental Shelf Kristien Schelfaut1 , Els Verfaillie1 & Vera Van Lancker1 1

Ghent University, Renard Centre of Marine Geology (RCMG), Krijgslaan 281, S8, 9000 Gent, Belgium

1. Introduction One of the most inspiring approaches to combine data coverages in view of habitat mapping is described in the paper of Roff et al. (2000). They proposed a marine landscape approach, which enables to map habitats based on geophysical features alone, but in the view that these are important in determining the nature of biological communities. In this approach, the biology is only used passively to verify the final results. The concept also anticipates on the growing realization that conservation at the scale of spaces or landscapes might be more reasonable than conserving individual species (Roff et al., 2000). The spaces concept requires a top-down manner of working (Laffoley et al., 2000), which is exactly what is proposed in the paper of Roff et al. (2000). The marine landscape approach is regarded a broad scale mapping technique and as mostly high resolution datasets are used on the Belgian Continental Shelf, it is extremely important to handle the datasets with care during processing, in order not to loose important information. This report summarizes the methods used to identify and map seabed marine landscapes on the Belgian Continental Shelf. The potential as well as the limitation of the method applied is highlighted.

2. Overview 2.1. Overview of the performed tasks • • • •

Defining a series of environmental data layers characterising the seabed. Processing the data into GIS for further analysis; Identification of meaningful thresholds by means of a classification; Production of seabed marine landscapes by means of summarising and querying the different datasets; Biologically validating the maps (by means of ground truth data).

2.2. Overview of the selection of different data layers After assessing the environmental parameters which have most influence on the ecology and the availability of suitable data sets, the following data layers were selected for further analysis (an overview is given in table 1).

Bathymetry: Full coverage Bathymetric Digital Terrain Model (DTM), modelled with a resolution of 80 m (Data source: IVA Maritime Services and Coast, Flemish Hydrography) Slope: Full coverage slope dataset derived from the DTM, modelled with a resolution of 80 m Maximum bed shear stress: Modelled maximum bed shear stress at 250 m (Data source: Management Unit of the North Sea Mathematical Models (MUMM)). Median grain size: Spatial distribution map of the median grain size based on the sedisurf@database (hosted by Ghent University, Renard Centre of Marine Geology) and interpolated by means of ‘Kriging with external drift’ (KED). The dataset has a resolution of 250 m. Bedforms: Full coverage vector data map of the major dune fields occurring on the Belgian Continental Shelf. Data derived from individual single beam measurements added with available side-scan sonar and multi beam data. Gravel fields: Manual demarcation of the occurrence of gravel patches on the Belgian Continental Shelf Table 1 Data sets, data structure and data source: overview

Data set Bathymetry

Data structure Raster

Slope

Raster

Maximum bed shear Raster stress Median grain size Raster Bedforms

Features

Gravel fields

Features

Data source Ghent University, Renard Centre of Marine Geology, based on data from the IVA Maritime Services and Coast, Flemish Hydrography Ghent University, Renard Centre of Marine Geology, based on data from the IVA Maritime Services and Coast, Flemish Hydrography Management Unit of the North Sea Mathematical Models (MUMM) Ghent University, Renard Centre of Marine Geology Ghent University, Renard Centre of Marine Geology Ghent University, Renard Centre of Marine Geology

The resultant data sets were analysed in a classification to derive a series of landscape types.

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To assess the biological validity of the resultant maps, sample data have been sourced (Ghent University, Marine Biology Section). Approximately 800 samples were available for being analysed against the landscape types to assess their ecological validity. This was undertaken by: • Predicting the expected biological character in terms of a range of habitat types for each landscape type; and • Interfacing the sample data with the defined landscapes in GIS to determine the actual relationship. This process allowed enabling conclusions about the validity of each landscape type.

3. Detailed methodology 3.1. Data collection and processing In order to map seabed habitats in the absence of sound biological data, relatively stable geophysical datasets must be selected, functioning as indicators of habitat types across ranges of scales. Selected datasets preferably reflect conditions present at the seabed. However, as the marine environment is quite dynamic, including seasonal fluctuations, a series of datasets will be needed (Roff et al., 2000). In fact, there are two options applicable to select datasets. At first, a hierarchy of geophysical features can be used. This hierarchy is quasi similar to the classification scheme as proposed by Roff et al. (2000, 2003) and the “ecosystem level” as proposed in the marine ecological framework by Zacharias et al. (2000). Their technique represents a holistic approach aiming at including as much datasets as possible. The other option concerns the elaboration of a framework, in which only biologically or management relevant datasets are incorporated. The main issue is then to examine first what features are most relevant, taking into account habitat preferences of particular species and literature (e.g. Snelgrove et al., 1994). There are many abiotic datasets which can be included and which are relevant towards habitats, but not all of them have the same importance. From literature (Roff et al., 2000, Zacharias et al., 2000), it is seen that most datasets could be divided into 3 groups, respectively reflecting information on topography, sedimentology and energy. Such datasets are considered the most crucial ones. Nevertheless, the potential set of datasets used to discriminate among habitat types must be determined by what can be mapped from readily available geophysical data. Moreover, the actual set of datasets chosen within any region will depend on the natural range of variation in each one. Some datasets might not be relevant in a specific region because they show too little variation and do not allow discriminating among habitat types. Most datasets available at the scale of the Belgian Continental Shelf are full coverage datasets. In order to comply with the different proposed options, it was aimed to include datasets reflecting information on topography, sedimentology and energy. As such, six datasets were compiled, integrated and analysed in a Geographic Information System1.

1

ESRI software, ArcGIS, Arcview 8.3 and Arcview 9.1

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3.1.1. Bathymetry and derived slope dataset Digital bathymetric data were used to identify the major topographic features at the scale of the Belgian Continental Shelf. According to Roff et al. (2000), depth is able to define the distribution of major biological communities. A digital terrain model (DTM) was compiled based on bathymetrical data from the IVA Maritime Services and Coast, Flemish Hydrography and completed with data from the Dutch and English Hydrographic Offices. The map was compiled and interpolated in GIS software with a resolution of 80 m. For end user’s needs an interpolation was also made at a 250 m resolution (Van Lancker et al., 2005). As illustrated in Figure 1, the Belgian shelf is relatively shallow. The seabed dips gently from 0 m in the coastal zone to 50 m in the offshore parts. The seabed’s surface shows a highly variable topography, consisting of sandbanks and swales. The numerous large sandbanks are more or less grouped in a parallel pattern.

Figure 1 Bathymetric Digital Terrain Model (DTM). Data compiled from IVA Maritime Services and Coast, Flemish Hydrography

The derived slope dataset is illustrated in Figure 2. Slopes often vary quite differently in different regions. According to Burrough et al., 1998, the best maps are produced by calibrating the class limits to the mean and the standard deviation of the frequency distribution at hand. As seen in

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the map (figure 2), Burrough’s classification makes the typical slope/swale pattern clear, but the image is noisier than its original dataset. In the framework of producing seabed marine landscapes it is more desirable to work with fewer classes. The class break values were chosen as follows: Class Weak slopes Moderate slopes Steep slopes

Values 0 – 0.47° 0.47 – 1.18 ° 1.18 – 5.67 °

Figure 2 Slope dataset (derived from the bathymetric Digital Terrain Model)

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3.1.2. Modelled maximum bottom shear stress The Management Unit of the North Sea Mathematical Models (MUMM) supplied a modelled maximum bed shear stress dataset with a resolution of 250 meters. The variable represents the flow-induced force acting on sand grains on the seabed and gives an estimate of the amount of sediment transported. In a first phase, this dataset only covered 70 % of the shelf (see figure 3).

Figure 3 Modelled maximum bed shear stress at 250 m (Data source: Management Unit of the North Sea Mathematical Models (MUMM)

3.1.3. Modelled seabed sediments: median grain size The mapping of the seabed sediments covering the whole of the Belgian Continental Shelf has resulted in a highly detailed distribution map of the median grain size (Verfaillie et al., 2006; Van Lancker et al., 2005). To obtain this full coverage dataset, a multivariate geostatistical technique called ‘kriging with external drift’ has been used. This interpolation technique involves a secondary variable (e.g. bathymetry) to assist in the interpolation. The resulting map (figure 4) is very realistic as the sediment distribution over the sandbanks and in the swales is clearly separated. More detailed information on this technique and how this map is compiled is found in Verfaillie et al. (2006). The resolution of the dataset is 250 meters. As often shown in literature (e.g. Van Hoey et al., 2004), there exists a strong ecological relation between the grain size distribution and the character of biological communities. This emphasizes and confirms the ecological relevance of this parameter.

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Figure 4 Spatial distribution map of the median grain size based on the sedisurf@database (hosted by Ghent University, Renard Centre of Marine Geology) and interpolated using the geostatistical method ‘Kriging with external drift’ (KED). Verfaillie et al., 2006 describe the methodology and results in detail.

The full coverage modelled median grain size dataset is classified into eight classes. These numerous classes are necessary due to the small and medium topographic differences seen on the Belgian shelf. Moreover, any classification of substrate data for habitat mapping should be biologically meaningful in such a way that benthic community types can be discriminated by the grain size classes (Alidina et al., 2003). Fewer classes can be useful for the further handling of the different datasets, but would inevitably generalize too much the subtle differences seen on the shelf.

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The table (table 2) below illustrates the classification of the seabed sediments as used for this study. Table 2 classification of the modelled median grain size dataset

Defined classes Silt to very fine sand Fine sand

Medium sand Medium to coarse sand Coarse sand

Values (µm) 0 – 150 µm 150 -200 µm 200 – 250 µm 250 – 300 µm 300 -350 µm 350 – 400 µm 400 – 600 µm > 600 µm

3.1.4. Modelled bedform features A new holistic bedform map was compiled for the purpose of this study (Figure 5). The data was derived from individual single beam measurements and was added with the available sidescan sonar and multibeam data. However, to arrive at a holistic map, the highly detailed digital terrain model was used to delineate the bedform zones outside the sandbank areas or where no previous data was available. The areas that are attributed with ‘no detailed information available’ are likely devoid of major bedforms. The whole process was done in ArcGIS in combination with sophisticated 3D software packages and allowed to attribute the different areas with detail (Van Lancker et al., 2005, Schelfaut, 2005).

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Figure 5 Spatial distribution map of bedform occurrences

3.1.5. Gravel fields Some gravel fields exist on the Belgian shelf and as such may form an important marine landscape. The macrobenthos of gravel beds is characterized by a specific set of species that may differ drastically from the surrounding sandy environments (Van Hoey et al., 2004). Still, their occurrence is far less known because these fields are not sampled quantitatively and qualitatively enough. Several field observations by means of video performed in the Hinderbanken region, confirmed the presence of gravel. Though they exist, it is difficult to map them at the scale of the shelf. Seismic investigations deduced that the thickness of the Holocene sediments on the shelf is less than 2.5 m in most of the swales. Further offshore, especially the swale areas of the southern part of the Hinder Banks have a thin Quaternary cover and are therefore likely characterized by a gravely floor (Lanckneus et al., 2001; Le Bot et al., 2003). Here, the manual delineation of the gravel fields (figure 6) is based on sedimentological information (the median grain size dataset) and a dataset containing information on the thickness of the Quaternary sediments.

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Figure 6 Occurrence of gravel patches on the Belgian Continental Shelf

3.2. Data analysis This part describes the route taken to come to a final seabed marine landscape map. After their selection, most parameters were converted from grid format to features. The classes of the different datasets were converted into polygons e.g. the layer of the median grain size resulted in 5 polygons (silt to very fine sand, fine sand, medium sand, medium to coarse sand, coarse sand, see table 1). Polygons have the advantage that they contain major attribute tables, containing a lot of information from the different datasets (depth values, median grain size values…). This makes the further handling of the datasets (summarizing, querying) easier. However, this data format contains also several disadvantages. The overlay of the different datasets to form a new set of polygons leads inevitably to the creation of ‘sliver’ polygons. These are polygons that are too small to stand on their own. As these tiny polygons are still tagged with the attributes of the combined layers, they must be adsorbed in the neighbouring polygons. The slivers need to be handled with care, as removal of too many slivers is responsible for an increase in the cumulative error (Alidina et al., 2003). Moreover, the boundaries between the different defined classes are crisp and definite.

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All original data sets were unclassified continuous data sets. Due to this, the classification and the analysis of the different datasets led to different outcomes, defining different types of seabed marine landscapes. Used classifications must retain small scale variations seen on the Belgian Continental Shelf. Since there is no commonly accepted classification technique, a standard classification method called ‘natural breaks’ was used for the classification of the different datasets. The technique identifies breakpoints by looking for groupings and patterns inherent in the data. Boundaries of the classes are set where there are relatively large jumps in the data values. To avoid complexity, it was decided to work with a limited amount of classes per dataset. This is illustrated in the overview table (table 3). All available datasets were annotated with unique attributes (metadata) and unioned in GIS. Metadata is essentially documentation of all parameters defining a dataset. The collation of metadata reduces information loss when the datasets are further processed in GIS. Once collected, most datasets were manipulated to ensure compatibility regarding data structure and projection. After this standardization, it was important to combine the layers in a meaningful way. The union command combines or merges features from different layers into one feature, while maintaining the original features and attributes. By unioning, a new layer is created, containing new polygon features made up from the boundaries of the input polygons that allow easier querying. From the resulting union layer, derived marine landscape types were identified. To assist in the demarcation of seabed marine landscapes into distinct types, key criteria such as the median grain size distribution, the value of the maximum bed shear stress, the degree of slope, the gravel percentage and the presence or absence of dune fields were used. Depth values were excluded from the queries because of its relative value. A criterion such as the ‘Bathymetric Position Index’ (BPI) as a measure of where a location, with a defined depth is relative to the overall landscape (Weiss, 2001, Iampietro et al., 2002, Lundblad et al, 2006.) would be more objective (Verfaillie et al., 2007). As there are no standards available for the naming of specified seabed habitats, it was decided to assign descriptive names to the defined units (table 3). Besides the different physical characteristics of the seabed marine landscapes, the table also shows the extent of the marine landscapes in square kilometres and as a percentage of the total study area. In total, 17 seabed marine landscapes were identified for the Belgian Continental Shelf. The distribution of these landscapes and the legend of the map are illustrated in figure 7 and 8. The resolution of the different datasets and more specifically, the dataset with the poorest resolution sets the accuracy of the final product. This means that the resolution of the seabed marine landscapes comes to 250 m.

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Figure 7 Map of the defined seabed marine landscape types on the Belgian Continental Shelf

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Seabed marine landscape types on the Belgian shelf circalittoral medium to coarse sediment plain coarse sediment on steep slopes medium to coarse sediment plain with dunes medium to coarse sediment plain without dunes

Figure igure 8 Legend of the seabed marine Landscape types

medium sand on weak to moderate slopes and dunes medium sand on weak to moderate slopes/ no dunes medium sand on weak to moderate slopes coastal high bed stress very fine sediment plain coastal very fine to fine sediment plain fine sand on steep slopes coastal low bed stress very fine sediment plain medium sand and steep slopes of major sandbanks offshore gravel patches weak to moderate slopes and dunes weak to moderate slopes and absence of dunes steep slopes and dunes steep slopes and absence of dunes

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Table 3 Seabed features classification and their main characteristics

marine landscape type code Offshore marine landscape types circalittoral medium to coarse sediment plain 1 coarse sediment on steep slopes 2 3 4 14 15 16 17

medium to coarse sediment plains with dunes medium to coarse sediment plains without dunes weak to moderate slopes and dunes weak to moderate slopes and absence of dunes steep slopes and dunes steep slopes and absence of dunes

bedforms medium grain size depth range

maximum bed stress

slope(°)

391

< 30 %

11,5

57

< 30 %

1,7

443

< 30 %

13

352

< 30 %

10,4

gravel area (km²) percentage

% of total BCS

present

> 400 µm

> 29 m

variable

weak - moderate slopes

variable

> 400 µm

variable

variable

steep slopes

present

350 - 400 µm

variable

variable

absent

350 - 400 µm

29 -42 m

variable

weak - moderate slopes weak - moderate slopes

present

no information

variable

no information

steep slopes

43

< 30 %

1,3

absent

no information

variable

no information

10

< 30 %

0,3

absent

no information

variable

no information

5

< 30 %

0,1

present

no information

variable

no information

steep slopes weak - moderate slopes weak - moderate slopes

26

< 30 %

0,8

Table 3 Seabed features classification and their main characteristics (continued)

maximum marine landscape type bedforms edium grain sizdepth range bed stress slope(°) code Transitional marine landscape types (zone in between coastal marine landscape types and offshore marine landscape types) medium sand on weak to moderate slopes and weak - moderate dunes present 300 -350 µm 17 - 36 m variable slopes 5 medium sand on weak to moderate slopes/ no weak - moderate dunes absent 300 -350 µm 23 - 29 m variable slopes 6 weak - moderate medium sand on weak to moderate slopes variable 250 -300 µm < 29 m variable slopes 7 medium sand and steep slopes of major sandbanks absent low steep slopes 12 250-400 µm variable weak - moderate offshore gravel patches variable low slopes 13 mixed 23 - 36 m Coatstal seabed marine landscape types absent high variable 8 coastal high bed stress very fine sediment plain 0 -150 µm 4 - 17 m weak - moderate coastal very fine to fine sediment plain variable 150 -250 µm < 17 m variable slopes 9 fine sand on steep slopes variable 150 - 250 µm 10 - 17 m variable steep slopes 10 absent low variable 11 coastal low bedstress very fine sediment plain 0 - 150 µm < 17 m

% of total gravel BCS area (km²) percentag

303

< 30 %

8,9

363

< 30 %

10,7

323

< 30 %

9,5

218

< 30 %

6,4

35

> 30 %

1

78

< 30 %

2,3

581 20 151

< 30 % < 30 % < 30 %

17,1 0,6 4,5

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3.3. Biological validation The purpose of this section is to test the ecological validity of the seabed marine landscape map of the Belgian shelf, derived from the physical data combinations only. The validation process consists of the following steps: • • • •

Collation of biological data for sites throughout the study area; Development of a prediction as to which biological community might be expected to occur within each defined seabed marine landscape; Compare ground-truth data with the predicted communities within the modelled landscape map; Interpretation of the results.

3.3.1. Collection of biological data Biological data is obtained from the Marine Biology Section (Ghent University). The Macrodat database (Macrodat, Marine Biology Section, Ghent University) contains more than 800 biological samples and those are analysed up to community and species level (Fig. 9). All samples are gathered in the framework of different research projects. However, the sampling locations are biased towards the sandbanks; as such sandy communities are oversampled. Fewer samples are available from the open sea and the eastern part of the Flemish Banks (Van Hoey et al., 2004). In general, the highest density (number of samples per km²) is found in inshore areas and decreases steadily in an offshore direction. The highly variable topography of the Belgian Continental Shelf also implies the occurrence of different macrobenthic assemblages. Macrobenthos is defined as plants or animals whose shortest dimension is greater than 1 mm. A community is defined as a group of organisms occurring at a particular place (a physico-chemical environment) interacting with each other and the environment. Distribution and diversity patterns are therefore linked to a specific habitat type. Up till now, four macrobenthic communities (three subtidal and one intertidal community) and six transitional communities (three subtidal and three intertidal species associations) are discerned on the Belgian Continental Shelf (Degraer et al., 2002) (Fig. 9). The occurring species associations differ drastically in habitat and species. The Macoma baltica community is bound to fine sandy, shallow locations characterized by high mud contents. They are merely found close to estuarine environments (De Waen, 2004). In near shore muddy sands, species of the Abra alba community are represented. The assemblage is characterized by a high abundance, as well as a high diversity. Within the community, bivalve species occur in high densities. They serve as an important food resource for epibenthic predators and benthic eating diving sea ducks (Degraer et al., 2002). The Nephtys cirrosa community is characterized by low species abundance and diversity. The last major macrobenthic community, Ophelia limacina, is found in medium to coarse sediments, often accompanied by gravel and shell fragments. However, the community is sometimes found in fine to medium sands with very low mud content.

Figure 9 The occurrence and distribution of macrobenthic communities on the Belgian Continental Shelf. The left hand figure illustrates the distribution of all biological samples taken on the Belgian Continental Shelf (Macrodat database, Ghent University, Marine Biology Section). The right hand figure illustrates the distribution pattern of the 4 major macrobenthic communities A (Macoma balthica), C (Abra alba), E (Nephtys cirrosa) and G (Ophelia limacina).

3.3.2. Predicting a correlation between habitat classes and landscape types The expected biological character of each landscape type was investigated on the basis of definitions as defined in Van Hoey et al. (2004). The presence of macrobenthos on the Belgian Continental Shelf is highly correlated with the physical environment. In Van Hoey et al., 2004, the correlation with the sediment mud content and the median grain size was investigated and confirmed. Table 4 gives an overview of the biological communities with their specific physical preferences. Table 4 Overview of the preferences of the biological communities present on the Belgian Continental Shelf (Degraer et al., 2002; Van Hoey et al., 2004).

Species assemblage Macoma balthica Abra alba Nephtys cirrosa Ophelia limacina

Median grain size 95 µm 219 µm 274 µm 409 µm

Mud content 36 % 6% 0,4 % 0,3 %

depth 6m 12 m 13 m >10 m

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As the medium grain size dataset has no full coverage at the moment of modelling the first marine landscape map, it was unable to predict the presence of specific communities in some defined landscapes. When the values of the medium grain size classes are not taken too strict, but more broadly based, then the following predictions are made for the different seabed marine landscapes (table 5): Table 5 Expected biological value of the defined habitat types. The predictions are based on the characteristics and the preferences of the defined macrobenthic communities as described in Van Hoey et al., 2004. No predictions are made for landscapes 13 to 17 due to the limitations of the median grain size datasets. Code

1 2 3 4 5 6 7 8 9

Expected macrobenthic community (prediction)

G G G G E E E A C

Code

10 11 12 13 14 15 16 17

Expected macrobenthic community ( prediction)

C A E -

3.3.3. Correlation between the defined seabed marine landscapes and ground truth data Available biological ground truth data provide a tool to validate whether the data used for the characterization of the seabed marine landscapes provide an accurate representation of the marine landscapes and also, that the marine communities observed, reflect those that had been predicted (Golding et al., 2004). The available amount of biological data was linked to the defined seabed marine landscapes using the spatial join command in GIS, which allows investigating the relationship between features in two joined layers. The command annotates the attribute table with a count field, allowing finding out the number of samples taken in each unit. In that context, the ecological units are validated towards their biological relevance. The distribution of biological data gives an indication of the support given by the sample data for each landscape type. As can be seen in figure 10, the biological samples are unevenly distributed across the seabed marine landscape map. Looking at the distribution pattern in a more detailed way, the samples only cover a small proportion of the area of each landscape type. In some cases, biological data are even completely absent. Because not every marine landscape covers an equal surface and as in no single case, the samples evenly cover the landscapes, it is valuable to get an idea of the density of the biological samples across the landscape map. The density map has been generated by means of a classification scheme based on a geometric array (De Maeyer et al., 2001). This is illustrated in figure 10. In particular, the highest density of sample data is found in the coastal zone. The number of samples gradually thins out into an offshore direction. 20

Figure 10 Distribution of the biological samples taken on the Belgian Continental Shelf (based on the Macrodat database, Ghent University, Marine Biology Section). The figure on the right hand side illustrates the density classification of the number of samples taken per km².

Figure 11 summarizes the number of biological samples per defined seabed marine landscape. As illustrated in the table, each bar is subdivided and shows the share of each species assemblage within the total number of observations.

Figure 11 Summary of the number of biological samples per defined seabed marine landscape

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From this table, it becomes clear that within every landscape several species assemblages are harboured, interacting with each other. Still, every marine landscape is dominated by one specific macrobenthic community. The global pattern of the communities is traced back in the seabed marine landscape map. The coastal marine landscapes, lying close to Zeebrugge harbour (8, 11), are mostly dominated by the Macoma balthica community. The other coastal ones (7, 9, 10) are mostly dominated by the Abra alba and the Nephtys cirrosa community and, to a smaller extent by the Ophelia limacina community. However the latter community shows preference to coarse sediments, this species assemblage is traced back near the coast of Oostende, due to sediment mixing and wave action. The transitional seabed marine landscapes (5, 6, 7, and 12) are mostly dominated by the Nephtys cirrosa community; however the Abra alba community is still present and even dominant in one landscape (medium sand on weak to moderate slopes/ no dunes; code 6). Although the Nephtys cirrosa community still occurs in the offshore seabed marine landscapes, they are mostly dominated by the Ophelia limacina community. Taken into account that the density of the ground truth data gradually decreases in an offshore direction, the correlation between the defined seabed marine landscapes and ground truth data seems to be acceptable. Still, it is worthwhile comparing the results of the expected (or predicted) presence of specific biological assemblages in the seabed marine landscape map and the outcome obtained by correlating the seabed marine landscapes and biological ground truth data (table 6). Table 6 Biological characterization of the defined seabed marine landscapes by means of biological data (Macrodat database, Ghent University, Marine Biology Section) compared to the expected biological value of the defined habitat types Code

1 2 3 4 5 6 7 8 9

Expected

G G G G E E E A C

Ground truthing

Resemblance

G G G G E/G C E A/C C

Yes Yes Yes Yes Yes No Yes Yes Yes

Code

10 11 12 13 14 15 16 17

Expected

Ground truthing

Resemblance

C A E -

C A E -

Yes Yes Yes -

The resemblance between the defined marine landscapes and the sample data (see table 6) is restricted and only reaches a correlation percentage of 65 % (11/17).

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4. Discussion and main conclusions The defined seabed marine landscapes had a clear relation with the terrain and as such they are valuable for different purposes (e.g. spatial planning related to aggregate extraction, for the prediction of seabird distribution). However, the correlation with the biological ground truthing data was somewhat blurred and the amount of landscapes seems rather unmanageable. Based on the available biological data it was not possible to validate each defined seabed marine landscape in the same way. Landscapes characterized by a lot of biological samples are well validated and reveal a high biological value. The biological value of the offshore landscapes is rather doubtful and in some cases not confirmed. This is caused by a general paucity of biological validation data in the offshore areas which led to insufficient data to validate the offshore landscape types. In the mean time, biology-steered habitat models (Degraer et al., 2002) put forward that 70 % of the distribution of the macrobenthic communities was determined by the median grain size and the remainder by the silt-clay percentage. In a subsequent phase, only those two parameters were used in the landscape approach. Five landscapes were defined and these correlated for 80 % with the existing biological ground truth dataset (Figure 12). However, the relation with the terrain is now more or less lost. Apart from the general distribution pattern, which can now be modelled in GIS, observations do learn that macrobenthic communities are bound to specific physical habitats.

Figure 12 Map of the defined seabed marine landscapes based only on the median grain size dataset (Verfaillie et al., 2006) and the modelled silt clay dataset (Ghent University, Renard Centre of Marine Geology).

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4.1. Limitations on geographic coverage As stated throughout the text, some datasets do not cover the whole study area, which has major consequences on the final product. This is also seen in figure 8, where the northern tip of the Belgian Continental Shelf is composed of a mosaic of small units, which do not connect the other defined landscape types. Those ‘landscapes’ are resulting from the combination of only 3 datasets (bedforms, slopes and bathymetry). As no information on the median grain size is available in those landscapes, they are considered to be less biologically significant. Meanwhile, updates of the modelled datasets became available. As the correlation with the sediment mud content and the median grain size is often investigated and confirmed, updates of those datasets are extremely valuable towards the mapping of marine landscapes as well as to other purposes (e.g. management purposes, spatial planning related to aggregate extraction, for the prediction of seabird distribution). The first dataset concerns an update of the modelled median grain size dataset. Within the first map (figure 5), the sediment distributions at the northern extremity of the Bligh Bank and the Fairy Bank were less reliable due to a lack of samples. During surveys in 2006, new sedimentological samples were gained, as a result of which a new sediment map is being modelled. Besides, a full coverage silt-clay map came available. The distribution of the silt-clay percentage is illustrated in figure 13.

Figure 13 Spatial distribution map of silt clay percentage based on the sedisurf@database (hosted by Ghent University, Renard Centre of Marine Geology) and interpolated using the geostatistical method ‘Ordinary Kriging’.

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A dataset demarcating potential gravel fields was newly compiled (Van Lancker et al., 2007) (Figure 14). As those areas attract a set of macrobenthic species that may differ drastically from the surrounding sandy environment, an update of this dataset seemed to be valuable in terms of their biological relevance.

Figure 14 Update of the occurrence of gravel fields on the Belgian Continental Shelf

Finally, a full coverage dataset of the modelled maximum bed shear stress became available (Figure 15).

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Figure 15 Modelled maximum bed shear stress at 250 m (Data source: Management Unit of the North Sea Mathematical Models (MUMM).

4.2. Limitations on resolution of format The resolution of the used datasets plays an important role in determining the accuracy of the final product (Ardron et al., 2002). The layer with the poorest resolution generally sets the accuracy of the end result even though some areas might be better than this. The seabed marine landscape map of the Belgian Continental Shelf reaches a resolution of 250 m although the bathymetry and its derived slope dataset have a resolution of 80 m. When the area of the defined landscapes is smaller than the layer with the poorest accuracy squared, it should be eliminated from the end result. Another complication of processing features is the creation of sliver polygons. These are small, narrow polygon features that appear along the borders of polygons following the overlay of two or more geographic datasets. An extensive description on the advantages and the disadvantages on using features is outlined in section 3.2.

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Given the availability of data and the relatively restricted dimensions of the Belgian shelf, both vector and raster approaches were trialled. Whereas in vector data structures the topology (referring to the relationship between adjacent features) between different units is explicitly recorded, this is only implicitly coded within raster datasets trough the attribute values in the pixels. Therefore, raster datasets are ’one attribute maps’. Compared to features, raster datasets cannot be tagged with multiple attributes. Still, the major processes of handling, preparing and querying the datasets remain comparable to working with vector data. Layers having different resolutions can be combined together. The resolution of the final raster dataset depends on the settings within Arcview’s Spatial Analyst extension. Setting the cell size smaller than the layer having the poorest resolution does not increase the accuracy of the final product. Within the UK Sea Map, all input data layers were converted into a vector grid. More information on this technique is found in Connor et al. (2006). Both approaches have advantages and disadvantages. It depends on the user’s choice whether features; raster or vector grids are applied. When there are many steps involved in generating the final map, it can be difficult to keep track of the assumptions, tools, datasets, and other parameter values used. The whole process of creating a marine landscape map is simplified by means of models, which can be constructed within the ArcGIS model builder. A model consists of one process or, more commonly, multiple processes strung together. It allows performing a work flow, modify it, and repeat it various times. Figure 16 illustrates a snapshot of the provisional model as applied on the Belgian Continental Shelf. All datasets used within the model builder were in raster format.

Figure 16 Snapshot of the provisional model as applied on the Belgian Continental Shelf to generate a seabed marine landscape map.

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The work undertaken to produce the seabed marine landscape maps on the Belgian Continental Shelf and to validate them with biological data has led to a map with different levels of quality as might be expected from a methodology using data with varying resolutions and complexity. Despite having gained good practical experience in developing a seabed marine landscape map for the Belgian part of the North Sea, there are still a number if challenges which are not anticipated yet. To ensure the maps remain useful in the future, both the overall quality of the input datasets and the marine landscape map itself need to be improved and maintained. The most important themes of mapping marine landscapes and the issues to improve and maintain the overall quality of the marine landscapes are described and highlighted in the UKSeaMap report (Connor et al, 2006). Summarized these are: • • • • • •

Quality and completeness of the underlying datasets Modifications to models Quality and completeness of biological validation data Refinement of the landscape classification Integration of the landscape map with finer scale habitat maps Development of a strategy and process for incorporating new data

5. References Alidina, H., Roff, J.C., 2003. Classifying And Mapping Physical Habitat Types (Seascapes) in The Gulf Of Maine and The Scotian Shelf: Seascapes Version to May 2003. WWFCanada and CLF Gulf of Maine / Scotian Shelf MPA Planning Project, Canada, 33 pp. Ardron, J.A., Lash, J., Haggarty, D., 2002. Modelling a network of Marine Protected Areas for the Central Coast of British Colombia. Version 3.1. Living Oceans Society, Sointula, British Colombia, Canada, 120 pp. Burrough, P.A., McDonnell, R.A., 1998. Principles of Geographical Information Systems. Oxford Univeristy Press, New York, 333 pp. Connor, D.W., Gilliland, P.M., Golding, N, Robinson, P., Todd, D., & Verling, E. 2006. UKSeaMap: the mapping of seabed and water column features of UK seas. Joint Nature Conservation Committee, Peterborough. De Maeyer, Ph., De Vliegher, B.M., 2001. Algemene en thematische cartografie. Academia Press, Gent, 179 pp. Degraer, S., Van Lancker, V., Moerkerke, G., Van Hoey, G., Vinx, M., Jacobs, P. & Henriet, J.-P., 2002. Intensive evaluation of the evolution of a protected benthic habitat: HABITAT.Final Report. Federal Office for Scientific, Technical and Cultural affairs (OSTC) – Ministry of the Flemish Community, Environment and Infrastructure. Department Waterways and Marine Affairs Administration, Coastal Waterways, 124 pp.

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De Waen, D., 2004. Het macrobenthos van slibrijkse substraten. Een ongekende gemeenschap van het Belgisch Continentaal Plat. Diss.lic.biology, Thesis, Universiteit Gent, 75 pp. Golding, N., Vincent, M.A., Connor, D.W., 2004. The Irish Sea pilot – Report on the development of a marine landscape classification for the Irish Sea. JNCC, 30 pp. Iampietro, P., and R. Kvitek, 2002. Quantitative seafloor habitat classification using GIS terrain analysis: Effects of data density, resolution, and scale. In Proceedings of the 22nd Annual ESRI User Conference. San Diego, CA, July 8-12. http://gis.esri.com/library/userconf/proc02 Lackneus, J., Van Lancker, V., Moerkerke, G., Van den Eynde, D., Fettweis, M., De Batist, M. & Jacobs, P., 2001. Investigation of the natural sand transport on the Belgian Continental Shelf (BUDGET) Final Report. Federal Office for Scientific, Technical and Cultural Affairs (OSTC), 104 pp. + 87 pp. Annex. Laffoley, D. d. A., Connor, D.W., Tasker, M.L., Bines, T. (2000). Nationally important seascapes, habitats and species. A recommended approach to their identification, conservation and protection. Prepared for the DETR Working Group on the review of Marine Nature Conservation by English Nature and the Joint Nature Conservation Committee. Peterborough, English Nature, 17 pp. Le Bot, S., Van Lancker, V., Deleu, S., De Batist, M., Henriet, J.-P., 2003. Tertiary and Quaternary geology of the Belgian Continental Shelf. PPS Science Policy, Brussels, 75 pp. Lundblad, E., D. J. Wright, J. Miller, E. M. Larkin, R. Rinehart, S. M. Anderson, T. Battista, D. F. Naar, and B. T. Donahue, 2006. A Benthic Terrain Classification Scheme for American Samoa. Marine Geodesy, 29 (2), 89-111. Roff, J.C., Taylor, M.E., 2000. VIEWPOINT: National frameworks for marine conservation – a hierarchical geophysical approach Aquatic conservation and freshwater ecosystems, 10, 209223. Roff, J. C., Taylor, M.E., Laughren, J. (2003). "VIEWPOINT: Geophysical approaches to the classification, delineation and monitoring of marine habitats and their communities." Aquatic Conservation: Marine and freshwater ecosystems, 13, 77-90. Schelfaut, K., 2005. Defining marine landscapes on the Belgian Continental Shelf as an approach to holistic habitat mapping. MSc Thesis. Renard Centre of Marine Geology, Ghent University: Gent, Belgium, 38 pp. Snelgrove, P. V. R. and C. A. Butman (1994). Animal Sediment Relationships Revisited - Cause Versus Effect. Oceanography and Marine Biology, Vol 32. London, U C L Press Ltd. 32, 111177. Van Hoey, G., Degraer, S., Vinx, M., 2004. Macrobenthic community structure of softbottom sediments at the Belgian Continental Shelf. Estuarine, Coastal and Shelf Science, 59, 599-613. Van Lancker, V., Deleu, S., Bellec, V., Du Four, I., Verfaillie, E., Fettweis, M., Van den Eynde, D., Francken, F., Monbaliu, J., Giardino, A., Portilla, J., Lanckneus, J., Moerkerke, G., Degraer, 29

S. (2005). Management, research and budgeting of aggregates in shelf seas related to endusers (Marebasse). Scientific Report Year 3. . B. S. Policy, 103 p. Van Lancker, V., Deleu, S., Bellec, V., Du Four, I., Schelfaut, K., Verfaillie, E., Fettweis, M., Van den Eynde, D., Francken, F., Pison, V., Wartel, S., Monbaliu, J., Giardino, A., Portilla, J., Lanckneus, J., Moerkerke, G. & Degraer, S., 2007. Management, research and budgeting of aggregates in shelf seas related to end-users (MAREBASSE). Final Scientific Report. Belgian Science Policy, in prep. Verfaillie, E., Van Meirvenne, M. & Van Lancker, V., 2006. Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas, Continental Shelf Research, 26 (19), 2454-2468. Weiss, A. D. 2001. Topographic Positions and Landforms Analysis (Conference Poster). ESRI International User Conference. San Diego, CA, July 9-13. Zacharias, M.A., Roff, J.C., 2000. A hierarchical ecological approach to conserving marine biodiversity. Conservation Biology, 14 (5), 1327-1334.

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