defining marine landscapes on the belgian continental shelf as

of 2

Title: Defining Marine Landscapes on the Belgian continental shelf as an approach to holistic habitat mapping

Author(s): Kristien Schelfaut (UGent)

Document owner: Els Verfaillie ([email protected])

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Workgroup:

MESH action: 4

Version: n/a

Date published:

File name: Defining Marine Landscapes as an approach to holistic habitat mapping.pdf

Language: English

Number of pages: 51 (including these pages)

Summary: The concept of the marine landscapes is a quite young and recent developed approach firstly proposed by Roff and Taylor (2000). The concept offers an alternative for habitat mapping which generally combines geophysical and biological data for the direct mapping of community types. Mapping of extensive areas does not allow the usage of all kinds of datasets since biological data are generally lacking at a large scale. Therefore, the marine landscape approach allows to map habitats relatively fast without the usage of biological data. Available biological data are generally only used passively to validate the marine landscapes in terms of their biological relevance afterwards. This study is a first attempt to divide the Belgian continental shelf into discrete ecological units, solely based on geophysical data as proposed by the assumption of Roff and Taylor (2000). Datasets containing information on bathymetry, slopes, median grain-size, bedforms, maximum bed stress and gravel fields were integrated into a Geographic Information System (GIS) and further processed. Based on those variables, seventeen seabed marine landscapes are distinguished so far. As the approach serves as an alternative for habitat mapping, the defined marine landscapes must assure that they are biologically relevant. To ascertain the biological relevance of the seabed marine landscapes, the defined units are validated by means of biological information. The biological data are derived from real biological samples on the one hand and from sediment parameters on the other hand. The validation revealed a moderate correlation between the marine landscapes and the biological communities present on the Belgian continental shelf.

Reference/citation: Schelfaut, K., 2005. Defining Marine Landscapes on the Belgian continental shelf as an approach to holistic habitat mapping. M.Sc. Thesis, Universiteit Gent, Belgium, unpublished.

Keywords: Marine Landscapes, abiotic variables, Belgian

continental shelf

Bookmarks:

Related information:

Academic year 2004 - 2005

Kristien Schelfaut

Thesis submitted to obtain the degree of

Master of Science in Advanced Studies in Marine and Lacustrine Sciences

Promotor: Dr. Vera Van Lancker Supervisors: Els Verfaillie Samuel Deleu

DEFINING MARINE LANDSCAPES ON THE BELGIAN CONTINENTAL SHELF AS AN APPROACH TO HOLISTIC

HABITAT MAPPING

Preface

i

PREFACE

The strange thing about writing a thesis is that it starts with a preface, but actually, writing a thank you

note is something where I end off with. Though, looking back at the three months of my study, time

has come to thank all people that helped me in realizing this thesis by supporting me all the way.

First of all, I would like to thank my promoter Dr. Vera Van Lancker especially for giving me this

great opportunity. It was really fascinating learning every day a bit more about all the opportunities

that GIS offers. Through her help I was able to realize this thesis.

I would like to thank Els for the support, patience when things got wrong, advice and ideas working

with GIS. I also thank Samuel, especially for the guidance working with Fledermaus. Furthermore, I

would like to thank all other people at the RCMG for their support.

A special note goes out to my parents. I would like to thank them for giving me the opportunity to

study this extra year and especially for always supporting and encouraging me. Thanks mum and dad

for helping me to realize the things I dream off.

Furthermore, I would like to thank Katrien, Veerle and Hilde for their support and encouragements

and Walter, for the motivation.

Besides, I thank all my friends for showing their interest and of course for all the fine and funny

moments. Natasha, for the laughter and the ‘funny cooking moments’ we had during our last year as a

student. I thank Claudia, for the jokes and also for the nice conversations we had during our thesis

work.

Kristien

Abstract

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ABSTRACT

The concept of the marine landscapes is a quite young and recent developed approach firstly proposed

by Roff and Taylor (2000). The concept offers an alternative for habitat mapping which generally

combines geophysical and biological data for the direct mapping of community types. Mapping of

extensive areas does not allow the usage of all kinds of datasets since biological data are generally

lacking at a large scale. Therefore, the marine landscape approach allows to map habitats relatively

fast without the usage of biological data. Available biological data are generally only used passively to

validate the marine landscapes in terms of their biological relevance afterwards.

This study is a first attempt to divide the Belgian continental shelf into discrete ecological units, solely

based on geophysical data as proposed by the assumption of Roff and Taylor (2000).

Datasets containing information on bathymetry, slopes, median grain-size, bedforms, maximum bed

stress and gravel fields were integrated into a Geographic Information System (GIS) and further

processed. Based on those variables, seventeen seabed marine landscapes are distinguished so far.

As the approach serves as an alternative for habitat mapping, the defined marine landscapes must

assure that they are biologically relevant.

To ascertain the biological relevance of the seabed marine landscapes, the defined units are validated

by means of biological information. The biological data are derived from real biological samples on

the one hand and from sediment parameters on the other hand.

The validation revealed a moderate correlation between the marine landscapes and the biological

communities present on the Belgian continental shelf.

Table of Contents

iii

TABLE OF CONTENTS

Preface___________________________________________________________________ i Abstract __________________________________________________________________ ii Table of contents ___________________________________________________________ iii

1. INTRODUCTION _______________________________________________ 1

2. PHYSICAL DESCRIPTION OF THE BELGIAN CONTINENTAL SHELF __________ 3

3. THE CONCEPT OF THE MARINE LANDSCAPES _________________________ 4

3.1 General description _____________________________________________ 4 3.2 Development of the marine landscape approach for the BCS_____________ 5

4. MATERIALS AND METHODS _____________________________________ 6 4.1 Data collation and data structure___________________________________ 6 4.2 Resolution of the used datasets ____________________________________ 7 4.3 Analysis and processing of essential geophysical information____________ 8

4.3.1 Bathymetry _____________________________________________ 8 4.3.2 Slopes _________________________________________________ 9 4.3.3 Seabed sediments: median grain-size _________________________ 10 4.3.4 Generalized bedforms _____________________________________ 12 4.3.5 Maximum bed stress ______________________________________ 13 4.3.6 Gravel fields ____________________________________________ 14

5. RESULTS ____________________________________________________ 16 5.1 Identification of the defined marine landscapes _______________________ 16 5.2 Physical characterization of the seabed marine landscapes ______________ 18 5.3 Evaluation of the defined marine landscapes by means of biological data___ 21

5.3.1 Introduction ______________________________________________ 21 5.3.2 Description of the macrobenthic communities on the BCS __________ 21

• Macoma balthica community _________________________ 22 • Abra alba community _______________________________ 22 • Nephtys cirrosa community___________________________ 22 • Ophelia limacina community _________________________ 22

Table of Contents

iv

5.3.3 Prediction based on the characteristics of the marine landscapes _____ 23 5.3.4 Validation of the defined marine landscapes by means of biological data_______________________________________________ 26

5.3.4.1 Validation of the defined marine landscapes by means of biological information derived from samples _________________________ 26

5.3.4.2 Validation of the defined marine landscapes by means of biological

data derived from predictions of sedimentological samples ______ 30 6. DISCUSSION ________________________________________________ 34 7. CONCLUSIONS_______________________________________________ 37 List of Tables____________________________________________________________ v List of Figures ___________________________________________________________ vi References ______________________________________________________________ vii

Introduction

1

1. INTRODUCTION

The sea has always been a multiple source for human beings, but ever-growing usage raises the

pressure on the seas and the coasts. Increased pressure may lead to degradation of the marine

environment and all other processes that maintain the marine ecosystem. Nevertheless, the seas around

north-west Europe are characterized by an exceptionally wide range of seabed habitats and a very rich

biodiversity (anonymous, 2005).

Seabed habitats are important for a variety of reasons. They function as spawning and nursery grounds

for several fish species, they fulfill a crucial role in the recycling of nutrients, they can play an

important role in maintaining the water quality and so on. Moreover, benthic organisms function as

important food sources for higher organisms. It’s in our own concern to conserve this richness.

For the development and implementation of a wide variety of management strategies and conservation

issues, knowledge of the seafloor habitats is necessary.

Habitat mapping is a multidisciplinary task and requires generally biological data as well as

geophysical data. On a small scale, data availability is mostly not restricted and enables integration of

both types of datasets. Considering nature conservation, it is often necessary to map extensive areas,

but, going in an offshore direction, the amount of available data is generally decreasing. Countries

containing extensive coastlines and marine territories have a disadvantage through this shortage of

data. To get around this difficulty, Roff and Taylor (2000) proposed a marine landscape approach,

which enables to map habitats based on geophysical features, in the concept that these are important in

determining the nature of biological communities. Moreover, there is a growing realization that there

should be a conservation at the scale of spaces or landscapes rather 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 and Taylor (2000).

The concept of the marine landscapes has been applied to the Irish Sea (Golding et al., 2004), and the

Joint Nature Conservation Committee (UK) is now leading an international marine habitat mapping

program entitled ‘Development of a framework for Mapping European Seabed Habitats’ (MESH),

which aims to produce seabed habitat maps for north-west Europe and develop international standards

and protocols for seabed mapping studies.

Looking at the available information on the Belgian continental shelf (BCS), generally inshore more

information is available. Close to the coast detailed information is available for habitats and species.

Further offshore, this amount decreases steadily. Physical data are more widely available and cover

Introduction

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larger areas. In this way, general habitat mapping at a scale of the BCS is rather difficult. The main

reason is not the paucity of biological information, but rather the uneven distribution of the samples

taken at the scale of the BCS.

This study lies in the framework of an international marine habitat mapping project (MESH) and aims

to set up a first attempt to divide the Belgian continental shelf into discrete ecological areas, based on

geophysical data solely. To accomplish this aim, data are integrated into a Geographical Information

System (GIS) and are processed to distinguish distinct marine landscapes. In contrary to general habitat

mapping, the marine landscape approach uses the available biological data in a passive way and will

only be considered in a last phase, to validate the defined marine landscapes in term of their biological

relevance.

Physical description of the Belgian continental shelf

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2. PHYSICAL DESCRIPTION OF THE BELGIAN CONTINENTAL SHELF

The Belgian continental shelf (BCS) is part of the Greater North Sea and is situated on the continental

Shelf of north-west Europe (Ospar Commission, 2000). The surface of the BCS is about 3600 km²,

which is hardly 0.6 % of the north-west European shelf (Denis, 1992).

The area is characterized by its relative shallowness. The seabed of the BCS dips gently from 0 to 50

m in the more offshore parts of the shelf. In the coastal zone, which is between 10 and 20 km wide,

depths range between 0 and 15 m. The median part is characterized by depths ranging between 15 and

35 m. Finally, depths increase towards the northern part of the shelf where they range between 35 and

50 m. The BCS reaches its deepest point at 46 m.

Between the Netherlands and Great Britain average depths lie between 20 m and 30 m (Ospar

Commission, 2000).

The surface of the seabed exists mainly of sandy sediments and shows a highly variable topography.

The topography consists of sandbanks and swales. A large part of the BCS is covered with numerous

large sandbanks grouped in a parallel pattern. The direction of the asymmetry can change going from

one end of a sandbank to the other. Some sandbanks are characterized by a central kink (Denis, 1992).

The sandbanks can be divided in four major groups: the Coastal Banks and the Zeeland Banks are

quasi parallel to the coastline, whereas the Flemish Banks and the Hinder Banks have a clear offset in

relation to the coast (Lanckneus et al., 2001). The depth and the characteristics of the seabed

sediments in the swales can differ along the two sides of a sandbank.

The interaction of the currents with the morphology of the BCS is responsible for the sorting of the

sediments. Generally, sediments coarsen in an offshore direction (Lanckneus et al., 2001). The sand

fraction (0.063-2 mm) is merely found on the sandbanks, whereas coarser sands, gravel (> 2 mm) and

higher silt-clay fractions (< 0.063 mm) are found in the swales (Lanckneus, et al., 2001).

The sandbanks as well as the swales are covered with ripples and dunes. The heights of the dunes

differ going from one region to another. Movement of water masses and the tides are responsible for

the displacement of the ripples and dunes.

At a large scale, water masses of the Greater North Sea consist of a mixture of North-Atlantic water

and freshwater runoff (Ospar Commission, 2000). Considering the BCS as such, the former

characteristic is less important. Close to the coast, onshore waters are influenced by runoff from the

mainland. Further offshore the influence of coastal runoff diminishes.

The concept of the marine landscapes

4

3. THE CONCEPT OF MARINE LANDSCAPES

3.1 GENERAL DESCRIPTION

In terrestrial environments, geophysical features such as climate and physiography (bottom relief and

sediment type) are used to identify and classify distinctive areas of a landscape. These features are

considered to be stable in timescales of hundreds of thousands of years and act as indicators of habitats

and community types across a range of scales (Roff et al., 2000).

Features used to define landscapes in terrestrial environments can also be used in a marine context,

however, the latter is considered to be more dynamic at spatial and temporal scales compared to

terrestrial landscapes. The physiographic and climatologic features used in terrestrial environments

contribute to the definition of marine communities, but due to the dynamics of the environment, such

as seasonal fluctuations and migrations, additional components are needed (Roff et al., 2000).

Biological communities in a region are the result of many interacting physical, chemical and

biological parameters. Direct mapping of community types requires biological data, but most of the

time and especially at larger scales, there is a paucity of biological data. Therefore, Roff & Taylor

(2000) proposed the concept to identify marine habitats solely based on geophysical data. Geophysical

data are used as a proxy for biological data in order to develop a classification for marine

environments (Vincent et al., 2003). This approach assumes a link between abiotic (landscapes) and

biotic (species) elements. The identified marine habitat types are referred to as ecological units,

seascapes or marine landscapes (Vincent, 2003; Laffoley, et al., 2000; Golding et al., 2004).

It is often not clear what exactly is meant with the term ‘habitat’. Definitions referring to the term

‘habitat’ seem to depend on the objectives and the datasets that are used to accomplish a particular aim

of a study.

Besides, there is no single article so far that compares general habitat mapping to the marine landscape

approach. Although things remain quite unclear, several aspects allow to see the major differences

between the two approaches.

The major similarity is that both aspects make use of biotic and abiotic features to characterize a

particular marine area. The marine landscape approach suggests working in a top-down manner, from

geophysical data trough biological data (Laffoley et al., 2000). According to Laffoley et al. (2000) the

likely scale for the marine landscape approach lies between 10’s to 10,000’s km². However, ICES

(2005) assumes that marine mapping based largely on physical characteristics of the environment acts

as a good surrogate for general habitat maps, they cannot be considered as true habitat maps as long as

no extensive validation is done.

The concept of the marine landscapes

5

Compared to the marine landscape approach, habitat mapping suggests working in a bottom-up

manner. Biological data are used actively to discriminate distinct assemblages of benthic species and

to correlate them with the seabed characteristics in order to distinguish benthic habitats (Kostylev et

al., 2001). Thus, biological data are used as guidelines for identification of habitat zonation (Kostylev

et al., 2001) whereas in the marine landscape approach the biological data is only used in a passive

way. Besides, the likely scales are significantly smaller compared to the marine landscapes and lie

generally between 0.01 and 100’s km² (Laffoley et al., 2000).

3.2. DEVELOPMENT OF THE MARINE LANDSCAPE APPROACH FOR THE BCS

Looking at the available information on the BCS, it becomes clear that the inshore areas are generally

far more known than the offshore parts. Close to the coast detailed biological information, bound to

sampling locations, is widely available. Generally, geophysical data are far more available than

biological data. These datasets are mostly compiled from acoustic instruments, from remote sensing or

other techniques. Most datasets, used in this study, cover more or less the whole surface of the BCS.

According to Laffoley et al. (2000), the marine landscape approach is applicable in regions with a

likely scale ranging from several 10’s to 10.000 km². These proposed boundaries are artificial, but

somehow give an idea of the scale where the marine landscape approach is applied to. The surface of

the BCS, which is about 3600 km ² is within this range.

The concept of the marine landscapes is as well applicable to the water column as to the seabed.

Creation of marine landscapes in the water column requires information about water temperature,

illumination, stratification or the mixing regime. However, compared to the Irish Sea, the BCS is not

characterized by significant fluctuations in water column processes and as such in this study only

seabed marine landscapes are developed. This requires information about bottom temperature,

substratum type, exposure and slopes. Much more other physical factors can be concerned and for a

holistic approach, the best thing to do is considering as many variables as possible.

Materials and Methods

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4. MATERIALS AND METHODS

4.1 DATA COLLATION AND DATA STRUCTURE

To draw a realistic picture of the seabed marine landscapes, it is necessary to compile relevant

datasets. It is important that the selected datasets contain information that characterize the seabed and

that they are relevant towards the characteristics of the biological communities.

In total, six datasets were used and further processed to create the seabed marine landscapes. Complete

coverage of the BCS was not obtained for a few datasets.

Datasets were integrated into a Geographic Information System (GIS). The GIS software used is

ArcGIS 8.3. Used datasets are summarized in Table 1.

Table 1 Data type and format of the available datasets

Data type Data structure Data source

Coverage of BCS

Coordinate system

Seabed sediments: median grain-size Raster

Ghent University, Renard Centre of Marine Geology 94,8% WGS 84

Bathymetry Raster

Ghent University, Renard Centre of Marine Geology, based on data from the Ministry of the Flemish Community Department of Environment and Infrastructure Waterways and Marine affairs Administration Division Coast Hydrographic Office 100% WGS 84

Slope Raster Idem 100% WGS 84

Generalised bedforms Features Idem 100% WGS 84 Maximum bed stress Raster MUMM 70% WGS 84

Geology thickness of Quaternary Features Ghent University, Renard Centre of Marine Geology 100% WGS 84

Biological data / sampled communities Points Ghent University, Marine Biology Section

0.2 samples per km² WGS 84

Biological data / predicted communities Points

Ghent University, Renard Centre of Marine Geology + Marine Biology Section

0.8 samples per km² WGS 84

Most datasets were readily available for further processing except for a holistic bedform map. The

latter has been compiled specifically for the study of the marine landscapes.


7

However most used datasets are provided in a raster format, it was decided to go for a vector data

structure as this will be the structure used to create marine landscapes along north-west Europe in the

MESH framework. Therefore it was necessary to convert some of the available datasets from raster to

features.

After conversion, the used datasets were further processed. Each attribute table was annotated with

unique attributes of properties of the datasets and merged with each other, using the union command

in GIS. This process combines all attributes of the used variables into one major attribute table and

allows easier querying in the GIS. The seabed marine landscapes are the result of a combination of

attributes from several used variables.

The usage of polygons has advantages as well as disadvantages. The main advantage is that using a

vector dataset, a feature can store several attributes whereas in a raster data structure, each feature can

store only one attribute.

A disadvantage working with a vector data structure is that the boundaries between different features

are crisp and definite. These crisp boundaries are not created working with raster datasets (Alidina et

al., 2003). The latter also allows to work with fuzzy boundaries and gives the opportunity to see a

gradual transition from one variable to another.

The main problem encountered using a vector data structure is the creation of slivers. Slivers are

polygons that are considered to be too small to stand on their own, and therefore must be adsorbed in

the neighbouring polygons. They are inevitably created when the boundaries of several combined

variables intersect. This means that through the union command tiny polygons are created which are

still tagged with all the attributes of the combined layers. Therefore it is important to handle those

polygons with care. Removal of too many slivers generalizes the result but decreases the accuracy of

the final product and therefore increases the cumulative error (Alidina et al., 2003). However, it is

important to keep in mind that both vector and raster data structures have potentials and limitations.

4.2. RESOLUTION OF THE USED DATASETS

Compared to the Irish Sea (Vincent et al., 2004; Golding et al., 2004) it was aimed to apply the marine

landscape approach in a more detailed way on the BCS. First, the scale of the BCS (3600 km²) is far

more limited than that of the Irish Sea, which covers an area of about 58000 km². Secondly, because of

the limited scale, the chance of gaining data covering the whole BCS is more realistic than in the Irish

Sea. Data gaps were more common in the Irish Sea. Thirdly, far more differentiation is seen in the

Irish Sea than on the BCS. Looking at the available datasets, it becomes clear that the differentiations

seen in slopes, grain-size, bed stress,…are far less than those along the Irish Sea. For instance, in the


8

Irish Sea, all types of substrata are present whereas the BCS shows no extreme sedimentological

differentiation. Using the same classifications as in the Irish Sea Pilot would lead to meaningless

results. As such, the approach needs to be more detailed. Therefore it is important that the datasets are

handled with care, in such a way, that after processing the datasets, small differentiations at the BCS

are still seen.

The bathymetry and slope dataset are provided with a resolution of 80 m. The maximum bed stress

and the median grain-size of the surficial sediment dataset have a resolution of 250 m.

The resolution 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 created polygons might be better than this. In practice, this means that the marine landscapes

defined in this study have an overall accuracy of 250 m. When the area of the defined landscape is

smaller than the layer with the poorest accuracy squared, it should be eliminated from the end result.

4.3. ANALYSIS AND PROCESSING OF ESSENTIAL GEOPHYSICAL INFORMATION

From the viewpoint that geophysical features are important in determining the nature of the biological

communities at a particular place and can be used as proxy for biological information (Roff et al.,

2000; Golding et al., 2004; Vincent et al., 2004), the selection of the different datasets will be decisive

in determining the quality of the end product. Therefore it is important to select datasets that are

relevant towards the biology.

Based on literature (Van Hoey et al., 2004), it often becomes clear that the distribution of the grain-

size is crucial in determining the presence of benthic communities at a particular place. Therefore, it is

a prerequisite to include a dataset that contains such information. It is of course not possible to

demarcate seabed marine landscapes solely based on grain-size distribution, and it is therefore

necessary to include other datasets as well. In this section an overview will be given of the different

datasets that have been used in determining the different seabed marine landscapes on the BCS.

4.3.1. Bathymetry

The bathymetry dataset is used in the benthic habitat classification because this dataset fulfills an

important role. However, depth is a highly variable criterion, other datasets are derivatives from this

dataset. Moreover, it reveals the variable and diverse topography of the BCS. The dataset is the only

one where full coverage was achieved.

The original dataset was available as a raster dataset and it was necessary to convert it into features.

The result is seen in Figure 1. Each polygon represents a depth class.


9

Figure 1. Processed bathymetry into depth classes.

4.3.2. Slope

The slope dataset, was available in raster format and it was necessary to convert it to a vector data

structure. Slopes show areas of irregular and rapid changes in the seabed topography. Because of the

small variations on the shelf, it is important that after processing the distinction between slopes and

relatively flat areas can still be made. Therefore, the converted dataset is divided in 2 distinct classes,

to clearly separate the steep zones from the surrounding area.

This dataset is compiled because it might be important towards the biological communities present on

the BCS. Some specific benthic communities could be associated with slope areas (Degraer et al.,

2002) and this might be important in the validation of the defined marine landscapes.


10

Figure 2. Processed slope dataset converted from raster to features.

4.3.3. Seabed sediments: median grain-size

The surficial sediments occurring on the BCS generally coarsen in an offshore direction (Lanckneus et

al., 2001) and their differentiations are often determined by the dynamics of near-bottom flows

(Brown et al., 2000; Todd et al., 2000).

Interaction of the sandbank-swale configuration with currents determines the hydraulic sorting of the

sediments. The sand fraction (0.063-2 mm) is merely found on the sandbanks, whereas coarser sands,

gravel (> 2 mm) and higher silt-clay fractions (< 0.063 mm) are found in the swales (Lanckneus et al.,

2001).

The importance of the median grain-size in determining the presence of specific benthic communities

is quoted in several studies (Van Hoey et al., 2004). Because of this important, it is necessary to obtain

a full coverage of this physical parameter. To obtain this full coverage, a multivariate geostatistic

technique called ‘Kriging with external drift’ was used (Verfaillie et al., in prep.). This interpolation

technique involves a secondary variable such as the bathymetry, slopes,…to precisely predict the

median grain-size (Verfaillie et al., in prep.).

Based on the median grain-size (D50) dataset (sedisurf@ dataset), eight classes were distinguished.

Generally, substrate plays a dominant role in influencing the distribution of benthic and demersal


11

community types. 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). Lesser classes would generalize too much the differences seen on the BCS.

Table 2 indicates the classification of the seabed sediments as used for this study.

Table 2. Classification of the median grain-size dataset as used for this study (expressed in µm).

Median grain-size (µm) Defined classes 0 - 150 µm silt to very- fine sand

150 - 200 µm fine sand 200 - 250 µm fine sand 250 - 300 µm medium sand 300 - 350 µm medium sand 350 - 400 µm medium sand 400 - 600 µm medium to coarse sand

> 600 µm coarse sand

The dataset was provided in a raster format and therefore converted to features. Each polygon

represents a grain-size class and follows more or less the topography of the BCS. Because the

conversion created a lot of small polygons (slivers), it was necessary to smooth the result using a filter

in the GIS. Smoothing is a way of tidying up the result, but it is important to keep in mind that

smoothing induces information loss. This is mainly seen along the boundaries of the BCS.

Figure 3. Median grain-size distribution, converted from raster to features.


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4.3.4. Generalised bedforms

The surface of the BCS is covered with sandbanks and dunes. They form the larger and main bedforms

observed on the BCS (Ashley, 1990; Lanckneus et al., 2001). Mapping of bedforms allows one to get

a better insight in the morphology of a region.

For the development of seabed marine landscapes at the BCS, it was necessary to compile a map

containing information on bedforms and more precisely on dunes. In literature, it is stressed that the

distribution of grain-size is a decisive parameter in determining the presence of biological

communities. However, it is important not to exclude other possible relationships. In this context, it

seemed interesting to investigate whether there is a relationship between the biology on the one hand

and the presence of dunes on the other hand.

The BUDGET map (Lanckneus et al., 2001) contained already information on dunes, but this

information was mainly restricted to areas relatively close to the coast. In the framework of this thesis,

it was important to get an overall view of the distribution of dune fields at the BCS especially when

the marine landscapes are validated towards their biological relevance.

New dune fields were added based on a detailed DTM of all single-beam registrations on the BCS.

The Digital Elevation Model (DEM) was imported in Fledermaus and this allowed to make 3D

profiles to determine the height of the dunes and demarcate dune fields. It is important to mention that

single-beam registrations do not allow to demarcate the dune fields very detailed. The area close to the

coast seems to be relatively flat, whereas in reality dune fields occur there. Based on the single-beam

registrations, it was mostly impossible to determine dune fields close to the coast. When multibeam

registrations were available, the dunes could be mapped in a more detailed way.

To make the map more useful, height classes were visualized and the boundaries proposed in the

BUDGET map (Lanckneus et al., 2001) were redefined. The classification used in the new map has

two additional intervals. The classes are respectively between 1-2 m, 2-4 m , 4-6 m and dunes higher

than 6 m.

Analysis of the map reveals that the sandbanks, as well as the swales, are covered with ripples and

dunes. The heights of the dunes differ going from one region to another. The highest dunes are

observed at the northern extremity of the Flemish Banks and in the Northern part of the Hinder Banks

region. Closer to the coast, the occurrence of bedforms is restricted and the sandbanks are generally

devoid of bedforms (Lanckneus et al., 2001).


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According to Ashley (1990), all dunes defined on the BCS can generally be classified as large (heights

between 0.75 m to 5 m) to very-large dunes (heights higher than 5 meter). Besides height, the size of

the dunes is measured in terms of their spacing (Ashley, 1990). Ashley proposes that large to very-

large dunes are generally characterized by a spacing between 10 and 100 m. The spacing for very-

large dunes must be, according to Ashley (1990), larger than 100 m. When both criteria are applied to

the defined dune fields the assumption cannot be confirmed as such.

Figure 4. Generalized bedforms, based on a DTM of all single-beam registrations on the BCS.

4.3.5. Maximum bed stress

Bed shear stress can be expressed as the frictional force exerted by the flow per unit area of the

seabed. It is expressed in N/m² (Soulsby, 1997). The shear bed stress forms an important quantity for

sediment transport purposes, because it represents the flow-induced force acting on sand grains on the

seabed.

Generally, in most parts of the coastal and shelf areas, both waves and currents play an important role

in sediment dynamics. Therefore the bed stress is enhanced under a combination of waves and

currents.


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Figure 5. Maximum bed stress, converted from raster to features.

This dataset was available in a raster data structure and converted into features. As seen in Figure 5,

the coverage of the dataset with a resolution of 250 m is incomplete. At this time, a 750 m grid with

full coverage is available, but usage of this dataset would have major implications on the overall

resolution of the marine landscapes. Based on the raster dataset, five classes were distinguished to

clearly make a distinction between low and high bed stress. During the compilation of the marine

landscapes, it was necessary to group some classes together, to clearly emphasis the difference

between high and low bed stress.

4.3.6. Gravel fields

Some gravel fields exist on the BCS and as such may form an important marine landscape. Still, their

occurrence is far less known because these fields are not sampled quantitatively and qualitatively

enough. Though they exist, it is difficult to map them at the scale of the BCS.

Seismic investigations deduced that the thickness of the Holocene sediments on the BCS 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). Gravel fields are indeed found where the thickness is

minimal. To demarcate the gravel fields on the BCS a dataset containing sedimentological information

was used together with a dataset containing information on the thickness of the sediments. The main

reason to include such a dataset is that these landscapes seems to be valuable in terms of their

biological relevance. 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).


15

After processing, all layers were merged with each other using the union command in GIS. The

combination of several attributes of component variables, derived through the union process defines

the marine landscapes through a querying of the attribute table.

The key criteria used were the median grain-size, maximum bed stress, slope, gravel percentage and

the presence of dunes. Depth was not included in the queries because of its relative value.

Taking into account that the resolution of the used datasets determines the accuracy of the end product,

it was necessary to tidy up the map. Most of the polygons with an area smaller than 62.500 m² (lowest

resolution squared) were eliminated by adsorbing them in the surrounding polygons.

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5. RESULTS

5.1 IDENTIFICATION OF THE DEFINED MARINE LANDSCAPES

In total, 17 seabed marine landscapes were identified for the BCS. This is illustrated in Figure 6.

Figure 6. Illustration of the defined seabed marine landscapes. Each marine landscape is indicated with a code.

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17

Legend1_coarse grains (400 - 600 µm + > 600 µm) / weak to moderate slopes

2_coarse grains (400 - 600 µm + > 600 µm) / steep slopes

3_medium grains (350 - 400 µm) / weak to moderate slopes / dunes

4_medium grains (350 - 400 µm) / weak to moderate slopes / no dunes

5_medium grains (300 - 350 µm) / weak to moderate slopes / dunes

6_medium grains (300 - 350 µm) / weak to moderate slopes / no dunes

7_medium grains (250 - 300 µm) / weak to moderate slopes

8_silt to very-fine sand (0 - 150 µm) / high bed stress

9_fine sand (150 - 250 µm) / weak to moderate slopes

10_fine sand (150 - 250 µm) / steep slopes

11_silt to very-fine sand (0 - 150 µm) / low bed stress

12_medium grain size (250 - 400 µm) / steep slopes

13_gravel fields

14_weak to moderate slopes / dunes

15_weak to moderate slopes / no dunes

16_steep slopes / no dunes

17_steep slopes / dunes

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5.2. PHYSICAL CHARACTERIZATION OF THE SEABED MARINE LANDSCAPES

The seabed marine landscapes are listed in Table 3. The table also summarizes the physical

characteristics of each defined type.

Table 3. Summary of the physical characteristics of each seabed marine landscape type.

However the depth is a highly variable feature, a depth band is included in the table to get a better idea

of the location of the marine landscapes on the BCS. These values are only illustrative and should not

be taken as definitive values.

In the framework of this study the grain-size was regarded as the most important variable towards the

biology (Van Hoey et al., 2004). However the BUDGET report (Lanckneus et al., 2001) used the Udden

Wentworth Scale to distinguish different grain-sizes of the surficial sediments present on the BCS,

similar ‘sediment bands’ can be distinguished in the map showing the seabed marine landscapes. Silty

to very-fine sediments can be found close to the port of Zeebrugge, fine sands (125-250 µm) are

encountered close to the coast, in areas shallower than 17 m and take up the first 16 km going in an

Marine landscape Depth (m) Substratum (µm) Maximimum bed stress Slope (°) Bedforms Gravel (%)1 > 29 m medium to coarse sand low to high weak to moderate dunes not applicableCODE 1 coarser than 400 µm2 variable medium to coarse sand variable steep variable not applicableCODE 2 generally > 400 µm3 variable medium sand low to high weak to moderate dunes not applicableCODE 3 between 350 - 400 µm4 between 29-42 m medium sand variable weak to moderate no dunes not applicableCODE 4 between 350 - 400 µm5 between 17 - 36 m medium sand variable weak to moderate dunes not applicableCODE 5 between 300 - 350 µm6 between 23 - 29 m medium sand variable weak to moderate no dunes not applicableCODE 6 between 300 - 350 µm7 shallower than 29 m medium sand variable weak to moderate variable not applicableCODE 7 between 250 - 300 µm8 between 4 and 17 m silt to very fine sand high variable no dunes not applicableCODE 8 between 0 -150 µm9 shallower than 17 m fine sand variable weak to moderate variable not applicableCODE 9 between 150 - 250 µm10 between 10 - 17 m fine sand low to high steep variable not applicableCODE 10 between 150 - 250 µm11 shallower than 17 m silt to very fine sand low variable no dunes not applicableCODE 11 between 0 -150 µm12 variable medium sand low steep no dunes not applicableCODE 12 250 - 400 µm13 between 23 -36 m variable low weak to moderate variable > 30 %CODE 1314 variable no information no information steep dunes not applicableCODE 1415 variable no information no information steep no dunes not applicableCODE 1516 variable no information no information weak to moderate no dunes not applicableCODE 1617 variable no information no information weak to moderate dunes not applicableCODE 17

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offshore direction. Medium to coarse sands (>250 µm) take up the remainder of the BCS. Especially

in the swales of the Hinder Banks, the grain-size is noticeable coarser than on the sandbanks.

Close to the coast and especially in the Western Coastal Banks area, surficial sediments are dominated

by fine to medium sands. The sands on the sandbanks are generally coarser than the grain-size

measured in the swales. The zones in between the sandbanks are characterized by a high percentage of

silt and clay. These characteristics are not seen on the map showing the seabed marine landscapes. A

possible explanation is the absence of a dataset containing such information.

Besides the grain-size distribution, morphological units can also be distinguished. The slopes of the

sandbanks are visible on the map. Moreover, a distinction can be made between the steep slopes of the

sandbanks close to the coast and those that are located in a more offshore direction. The slopes of the

sandbanks close to the coast are characterized by fine sands (125-250 µm) whereas those further

offshore are characterized by medium to coarse sands (>250 µm).

The outermost northern tip of the BCS is characterized by a highly variable topography. This is seen in

the presence of steep slopes and is emphasized by the patchiness of the polygons.

Based on the map of the seabed marine landscapes, all important morphological and sedimentological

characteristics can be distinguished.

Based on Table 3 an interesting finding concerning the ‘reliability’ of each marine landscape can be

reported. Most of the defined marine landscapes are developed based on as much attributes as

possible.

When all attributes of all used variables are present in the attribute table, it is possible to put them all

together in one query. When data were not available, full querying was not possible and the resulting

landscapes were characterized by a limited set of attributes. To some extent, these landscapes are less

reliable than those created based on all attributes and give a less realistic view.

In this way, it becomes clear that usage of data with an incomplete coverage influences the result in a

significant way.

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Table 3 only gives a description of the seabed marine landscapes. It is also important to get an idea of

the extent of each marine landscape. This is illustrated in Table 4.

Table 4. Extent of each seabed marine landscape. The values are ranked in a descending order.

Description Code area (km²) percentage of the

BCS weak slopes / fine sand (150-250 µm) 9 581 17,1 weak slopes / medium grains (350-400 µm) / dunes 3 443 13 weak slopes / coarse grains (>400 µm) 1 391 11,5 weak slopes / medium grains (300-350 µm) / no dunes 6 363 10,7 weak slopes / medium grains (350-400 µm) / no dunes 4 352 10,4 weak slopes / medium grains (250-300µm) 7 323 9,5 weak slopes / medium grains (300-350 µm) / dunes 5 303 8,9 steep slopes / medium sand (250-400 µm) 12 218 6,4 silt to very fine sand (0-150 µm) / low bed stress 11 151 4,5 silt to very fine sand (0-150 µm) / high bed stress 8 78 2,3 steep slopes / coarse grains (>400 µm) 2 57 1,7 weak slopes /dunes 14 43 1,3 gravel fields 13 35 1, steep slopes / dunes 17 26 0,8 fine sand (150-250µm) / steep slopes 10 20 0,6 weak slopes / no dunes 15 10 0,3 steep slopes / no dunes 16 5 0,1

Based on this table, it becomes clear that only 4 marine landscapes cover more than 50 % of the BCS.

These 4 landscapes determine in some way the dominant characteristics of the seabed of the BCS. The

last five marine landscapes only cover 3 % of the total area of the BCS. This does not mean that those

marine landscapes are less important towards the biology. This will be further investigated in

paragraph 5.3.

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5.3 EVALUATION OF THE DEFINED MARINE LANDSCAPES BY MEANS OF BIOLOGICAL

DATA

5.3.1 Introduction

Based on the assumption of Roff and Taylor (2000), the marine landscape approach can be used as a

surrogate to characterize the biotic environment. As such, based on the characteristics of the defined

geophysical marine landscapes, it is possible to make a prediction towards the biology. To be sure that

those predictions are acceptable the approach does need validation (Golding et al., 2004; Vincent et

al., 2004).

To check the relation between the marine landscapes and the biological data, both datasets were

coupled, using the spatial join command in GIS.

According to Van Hoey et al. (2004), a short overview of the macrobenthos on the BCS will be given

first. Based on these characteristics, the habitat preferences of the macrobenthic communities will be

compared to the characteristics of the defined marine landscapes. This allows to make a prediction of

the benthic communities that can be expected in each marine landscape. If the principles outlined by

Roff and Taylor (2000) are valid, then the results from the prediction must fit the results from the

biological characterization of each marine landscape.

This is outlined in a last section, where the prediction will be validated using biological information

derived from samples on the one hand and from predictions on the other hand (Table 1). The latter

represent biological information purely derived from a prediction based on median grain-size and silt-

clay content (Degraer et al., 2005).

5.3.2. Description of the macrobenthic communities on the BCS

As mentioned earlier, the BCS has a highly diverse topography. Due to this variability, different

macrobenthic assemblages are distinguished. According to Levinton (2001), macrobenthos can be

defined as benthic organisms (animals or plants) whose shortest dimension is greater than or equal to

0.5 mm. Generally, macrobenthos is defined as animals or plants whose shortest dimension is greater

than 1 mm (pers. comm.).

On the BCS, four macrobenthic communities (three subtidal and one intertidal community) and six

transitional species associations (three subtidal and three intertidal species associations) were

discerned so far (Degraer et al., 2002). A community is a group of organisms occurring at a particular

place (physico-chemical environment) interacting with each other and with the environment. This

means that the distribution and the diversity patterns are linked to the habitat type. On the BCS, the

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22

median grain-size and the mud content are the most discriminating parameters towards the prediction

of benthic communities (Van Hoey et al., 2004). This emphasis again the organism-sediment

relationship and the necessity to include a dataset containing information concerning the grain-size

distribution.

The species associations, considered in this study, differ drastically in habitat and species composition

and can be considered to represent four macrobenthic communities. Table 5 gives a summary of the

preferences of the median grain-size and depth of the macrobenthic communities occurring on the

BCS (Van Hoey et al., 2004; De Waen, 2004):

Macoma balthica community

The Macoma balthica community is bound to shallow locations and merely found in an estuarine

environment (De Waen, 2004). This species assemblage is characterized by its preference for a fine

sandy habitat (median grain-size is 95 µm) with a high silt content (36 %). The Macoma balthica

community can be found in waters with an average depth of about 6 m.

Abra alba community

This community is an ecologically highly valuable macrobenthic community on the BCS which is

found in near shore shallow muddy sands (depths around 10 meters) with a median grain-size of about

219 µm and a mud content of 6 %. The assemblage is characterized by a high abundance and diversity.

Within this community, bivalve species do occur in high densities. The bivalves are known to be an

important food resource for larger epibenthic predators and benthic eating diving seaducks (Degraer et

al., 2002).

Nephtys cirrosa community

Nephtys cirrosa communities fulfill a central role on the BCS and are characterized by a low species

abundance and species diversity. They occur in well sorted mobile sands where the median grain-size

is 274 µm. Generally, they are found in several transitional species associations.

Ophelia limacina community

This benthic community, characterized by a low diversity and species abundance, is found in the

sediments of the sand banks in the Southern North Sea at depths greater than 10 m. They are mainly

found in medium to coarse sandy sediments with a median grain-size of 409 µm. The sediments where

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23

they are found are often accompanied with gravel and shell fragments. The Ophelia limacina is also

found in fine to medium sands with a very low mud content.

Table 5 Overview of the preferences of the biological communities present on the BCS (Degraer et al., 2002;

Van Hoey et al., 2004)

Species assemblageMedian grain-size Depth

Mud content

Macoma balthica 95 µm 6 m 36%Abra alba 219 µm 12 m 6%Nephtys cirrosa 274 µm 13 m 0,4%Ophelia limacina 409 µm >10 m 0,3%

In between those 4 major macrobenthic communities transitional groups can be defined.

5.3.3 Prediction based on the characteristics of the marine landscapes

Based on the physical characteristics of the seabed marine landscapes and the information of the

benthic communities derived from literature, a prediction can be made of what kind of biological

communities can be expected in a particular marine landscape. It needs emphasis that for this

prediction no samples were used.

Van Hoey et al. (2004) included a depth criterion besides the median grain-size to describe the

preferences of macrobenthic communities on the BCS. However, the inclusion of a depth criterion in

determining macrobenthic communities on the BCS is relatively subjective and should not be

considered as a primary variable compared to other criteria such as the median grain-size distribution.

In total 17 seabed marine landscapes were determined (Figure 6). However, information concerning

the median grain-size as well as many other parameters are restricted to the first 12 marine landscapes.

Therefore, it was not possible to make predictions for the seabed marine landscapes with codes 13 to

17 (see Table 3).

When the depth and the grain-size distribution are both taken into account to predict possible benthic

communities in each marine landscape, only 35% of the marine landscapes seem to harbour

macrobenthic communities (Table 6). When the marine landscapes with codes 13 to 17 are excluded in

this calculation, a prediction can be made for 50 % of the seabed marine landscapes.

The marine landscapes with codes 8 and 11 meet the conditions to harbour the Macoma balthica

community. The marine landscapes are found relatively close to the coast and this confirms that this

community is found in quite shallow water. Moreover, the Macoma balthica community seems to

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24

show a preference for estuarine waters (De Waen, 2004). The Westerschelde influences the seawater

up to Oostende and can be a plausible explanation for this last requirement. No other benthic

communities are expected in those landscapes.

Two landscapes that completely meet the conditions to harbour the Nephtys cirrosa community are the

marine landscapes with codes 7 and 12. Codes 9 and 10 completely fulfil the conditions to contain the

Abra alba community. Finally, the Ophelia limacina community can be expected in the marine

landscapes with codes 1 and 2.

The prediction shows that a lot of the defined marine landscapes do not harbour communities when the

depth and grain size criteria are strictly applied. If they meet both the depth and grain size criteria, it is

not possible to predict several communities in one marine landscape and this is rather illogical. Based

on literature (Van Hoey et al., 2004; De Waen, 2004) it becomes clear that several communities

interact and occur in the same habitat.

When the grain-size distribution is considered to be the most crucial parameter in determining the

presence of the macrobenthic communities on the BCS, it is possible to make a prediction in 47 % of

the defined seabed marine landscapes (Table 6). When the marine landscapes with code 13 to 17 are

again excluded from the calculation, a prediction can be made in 67 % of the defined seabed marine

landscapes.

Table 6 Summary of the calculation of the expected communities in each defined seabed marine landscape.

Marine landscapes (code)

Prediction based on depth and grain-size distribution

Prediction based on grain-size distribution only

1 Ophelia limacina Ophelia limacina2 - Ophelia limacina3 - -4 - -5 - -6 - -7 Nephtys cirrosa Nepthys cirrosa8 Macoma balthica Macoma bathica9 Abra alba Abra alba10 Abra alba Abra alba11 Macoma balthica Macoma bathica12 - Nepthys cirrosa13 - -14 - -15 - -16 - -17 - -

Sum 6/17 8/17Percentage 35% 47%Exclusion of the marine landscapes with codes 13 to 17:Sum 6/12 8/12Percentage 50% 67%

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Based on literature (Van Hoey et al., 2004), small shifts in the depth as well as the grain-size

distribution can occur, due to temporal variations. When the defined classification of the median grain-

size is not strictly interpreted it is possible to make predictions in each defined seabed marine

landscape. This is indicated in Table 7.

Table 7 Summary of the expected communities in each defined seabed marine landscape. The benthic communities indicated in bold are predicted based on the grain-size criterion. The others represent predictions when the criterion is more fuzzy interpreted.

Code of the marine landscape Expected communities

1 Ophelia limacina community 2 Ophelia limacina community 3 Ophelia limacina community 4 Ophelia limacina community 5 Nepthys cirrosa community 6 Nepthys cirrosa community 7 Nephtys cirrosa community, 8 Macoma balthica community 9 Abra alba community 10 Abra alba community 11 Macoma balthica community 12 Nephtys cirrosa community 13 - 14 - 15 - 16 - 17 -

Summarized, it may be said that based on strict depth and medium grain-size criteria it was not

possible to predict communities in 65 % of the seabed marine landscapes. This is not a complete

satisfactory prediction and to get better results, it is necessary to exclude marine landscapes that do not

contain information concerning the grain-size distribution. Also, better results are obtained when the

boundaries of the median grain-size classification are taken less strict. When the latter assumption is

taken into account it is possible to predict communities in 70 % of the defined seabed marine

landscapes.

Therefore, a verification is also needed using the available biological information based on samplings

because they act as a ‘ground-truthing mechanism’ and actually show what kind of communities are

found at a particular location.

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5.3.4 Validation of the defined marine landscapes by means of biological data

Available biological data provide a tool to validate whether the data used for the characterisation of

the seabed marine landscapes provides an accurate representation of the marine landscapes, as they

actually exist and also that the marine communities observed, reflect those that had been predicted

(Golding et al., 2004).

This kind of validation will be done by means of data derived from biological samples on the one hand

and predictions based on the median grain-size and silt-clay on the other hand. Both results will be

compared to the predictions made in the previous section and will give an idea of the biological

relevance of each defined seabed marine landscape.

5.3.4.1 Validation of the defined marine landscapes by means of biological

sample data

Usage of the spatial join command in GIS allows to investigate the relationships between features in

two layers. In this study, available biological information was spatially joined to each marine

landscape. Figure 7 illustrates the distribution of the biological samples taken at the BCS.

Figure 7. Distribution of the biological samples taken at the BCS (based on Macrodat database, Ghent University, Marine Biology Section).

The spatial join command annotates the attribute table with a ‘count field’ and allows to get an idea of

how many samples are taken in a particular marine landscape.

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Because not every marine landscape covers a similar area (see Table 4) it is valuable to get an idea of

the density (number of observations per km²). It is generally assumed that a higher density gives a

more realistic picture of the reality and it is therefore more reliable to draw conclusions towards the

biological characteristics of the defined marine landscapes.

To classify the marine landscapes, a classification based on a geometric array was used (De Maeyer et

al., 2001). Based on the calculations the defined marine landscapes were divided in three classes (see

Figure 8).

Figure 8 Density classification showing the number of samples taken per km² (based on Macrodat database, Ghent University, Marine Biology Section).

The figure clearly illustrates that the number of samples and therefore the density decreases in an

offshore direction.

Figure 9 shows a summary of the number of observations per marine landscape. Each bar is

subdivided and shows the share of each species assemblage within the number of observations. Based

on that figure, some interesting findings can be reported.

Each marine landscape harbours several communities. This confirms the finding that several

communities or species assemblages occur at a particular place and interact with each other. However,

each marine landscape seems to be is dominated by one specific community.

The marine landscapes with codes 9, 7 and 1 are characterized by the highest number of observations.

If the density is high enough, the dominant community can be considered as a key community to

biologically characterize each marine landscape.

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0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17marine landscape (code)

num

ber

of o

bser

vatio

nsOphelia limacina (code G)Nephtys cirrosa (code E)Abra alba (code C)Macoma balthica (code A)

Figure 9 Number of observations per marine landscape

The resemblance between the biological samples on the one hand and the predictions made in the

previous paragraph seems to be relatively low when transitional species assemblages are taken into

account (see Table 8). When all marine landscapes, are considered, only 47 % of the defined marine

landscapes reflects the characteristics to harbour particular biological communities. When the marine

landscapes with codes 14 to 17 are excluded from the calculations a 66 % resemblance is found.

When the transitions are excluded from the calculations, and only the four macrobenthic communities

are considered as such, the result seems to be much better. When all marine landscapes are considered,

a resemblance of 65 % is achieved. Exclusion of the marine landscapes with codes 13 to 17 increases

the resemblance to 92 % (Table 8).

It needs emphasis that the resemblance between the biological data on the one hand and the

geophysical characteristics of the marine landscapes on the other hand needs to be interpreted with

care. As mentioned before, small shifts can occur in depth as well as in the grain-size distribution.

Therefore, it seemed acceptable to interpret the defined boundaries to be more fuzzy. However, when

the defined boundaries are interpreted as such, the above mentioned resemblances seem to be less

promising. The resemblance is then lower than 50 %.

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The discrepancy between both calculations is not negligible and must be taken into account when

conclusions are drawn towards the biological relevance of each marine landscape.

Beside that, it is also important to take the density into account. Based on the density the biological

relevance is high for the marine landscapes with codes 7, 8, 9 and 10. They are characterized by a high

number of samples and therefore give a representative image of what actually occurs on the seabed.

Although the predictions made for the marine landscapes 1, 2, 4 fit the biological characterization,

their biological relevance is lower because of the low number of samples taken per km².

Table 8 Biological characterization of the defined seabed marine landscapes by means of biological data derived from samples

Marine landscape

(code)

Present communities based on biological samples

Dominant community (including transitional groups)

Dominant community (excluding transional groups)

Prediction based on the characteristics of the marine landscapes

Resemblance (inclusion of transitional groups))

Resemblance (exclusion of transitional groups)

1 G, F, H, E G G G 2 G, F, E G G G 3 F,G, E, H F G G - 4 G, F, E G G G

5 F, G, E, D,

B F E/G E - 6 F, C, E, G F C E - -

7 E, C, F, D,

G E E E 8 A, C, D, G A/C A/C A

9 C, E, F, D,

B, A, G C C C 10 C, D, E, F C C C 11 A, C, D, B A A A

12 F, E, G, D,

C F E E - 13 F F - - - -

14 no

information - - - - -

15 no


16 no


17 no


Sum 8/17 11/17 Percentage 47% 65% Exlusion of the marine landscapes 13 to 17 Sum 8/12 11/12 Percentage 66% 92%

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5.3.4.2 Validation of the defined marine landscapes by means of biological data derived

from predictions of sediment parameters

To validate the defined seabed marine landscapes another biological dataset derived from

sedimentological data (median grain-size and silt-clay percentage) was used (Degraer et al., 2005).

As seen in Figure 10 the coverage is more complete than the coverage in Figure 7, but somehow it is

important to keep in mind that this dataset is less reliable than that used in previous paragraph, because

the information it contains is only a prediction towards the biology. Nevertheless, it is worth alike to

investigate this dataset and use it as a tool to validate the marine landscapes.

Figure 10 Distribution of the biological communities based on sediment variables (median grain-size and silt-clay percentage)

Figure 11 shows the number of predictions per marine landscape. Each bar is subdivided, showing

several communities. Compared to Figure 9, similarities as well as differences can be noticed. The

major difference between both datasets is that the latter does not contain transitional species

assemblages. Moreover, each marine landscape tends to be dominated by the Nephtys cirrosa

community. This dominance can be explained by the sandy nature of the BCS on the one hand and the

location of the samples on the other hand. Mostly, the sediment samples are limited to the sandbanks,

by where the deeper parts are often undersampled (Van Lancker et al., 2005). Whereas no biological

information was available for the marine landscapes with codes 13 to 17, the dataset predicts the

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occurrence of the Ophelia limacina community in the marine landscape with code 14. The main

similarity is that no information is available for the marine landscapes with codes 15 to 17.

Table 8 allows to compare the results of this biological dataset to the predictions solely based on the

physical characteristics of the defined marine landscapes. Because both datasets used the median

grain-size to predict possible biological communities, the resemblance is expected to be relatively

higher.

When all marine landscapes are considered a resemblance of 59% is achieved. When the marine

landscapes with the codes 13 to 17 are again excluded from the calculations, a resemblance of 83 % is

achieved.

Again, the proposed boundaries of the prediction, made solely based on the geophysical characteristics

of the marine landscapes, are not considered as such. A strict application of the defined boundaries

shows again a much lower resemblance (lower than 50%) and this needs to be taken into account

when conclusions are drawn.

0

100

200

300

400

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17marine landscape (code)

num

ber

of p

redi

ctio

ns

Ophelia limacina (code G)

Nephtys cirrosa (code E)

Abra Alba (code C)

Macoma balthica (code A)

Figure 11 Number of observations per marine landscape based on sediment variables (median grain-size and silt-clay content)

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Table 9 Biological characterization of the defined seabed marine landscapes by means of predictions from sedimentological samples

Marine Landscape

(code) Predicted Communities

Dominant community

Prediction based on the characteristics of the marine landscapes Resemblance

1 G, E, C G G 2 G,E G G 3 G, E G G 4 G, E, C G G 5 E, G, C E E 6 E, G, C E E 7 E, C, G E E 8 A, C, E, G A A 9 E, C, G, A E C -

10 E, C, G E C - 11 A, C, E A A 12 E, G, C E E 13 E, G, C E - - 14 G G - - 15 - - - - 16 - - - - 17 - - - -

Sum 10/17 Percentage 59% Exclusion of the Marine Landscapes with codes 13 to 17 Sum 10/12 Percentage 83%

Because the steep slopes form an important seabed marine landscape as such, they are already

validated in terms of their biological relevance. However, it is not possible to say that one specific

benthic community is associated with steep slopes.

Another important question concerns the possible relationship between dune fields on the one hand

and the occurrence of biology on the other hand. Dune fields are not considered as a seabed marine

landscape as such. Mostly, the presence of dune fields has been included as an attribute in the

characteristics of the seabed marine landscapes. However, it seems interesting to filter the dune fields

out of the union layer and investigate if there is a possible correlation with the biology.

The characteristics of the dune fields are highly variable. The heights, the grain-size distribution as

well as the depth and the bed stress values range from one dune field to another. They are encountered

on the steep slopes as well as in relatively flat areas. Beside that, it seems that the dunes are widely

spread at the scale of the BCS. They are mostly found in the marine landscapes with codes 1, 2, 3, 5

and 12. They are also encountered in other marine landscapes as well, but the occurrence is not so

striking there.

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Because of their uneven spreading, not each dune field can be characterized by means of biological

samples. Moreover, the number of samples as well as their distribution is highly variable. However the

Ophelia limacina community covers the highest number in the samples, it would not be

straightforward to consider this community as the most dominant species assemblage encountered in

dune fields.

Investigation of the interrelationship between the bed stress and the presence of biology does not allow

to draw striking conclusions. It would be wrong to draw conclusions for the whole area of the BCS

because the dataset does not cover the whole surface. The only striking finding is that no biological

communities are found in areas with a high bed stress.

Discussion

34

6. DISCUSSION

In Europe, the concept of the marine landscapes has been applied to the Irish Sea and in the framework

of this study developed for the BCS.

The approach offers quite potential when the usage of abiotic features as surrogates for patterns of

biodiversity is valid. This correlation is proposed in several papers (Zacharias et al., 2000; Roff et al.,

2000) and it is shown that such correlation is indeed present. However, it is not always possible to

check this correlation (Stevens et al., 2004). Most of the time biological data are relatively sparse at

larger scales and offer only a validation of the defined marine landscapes closer to the coast. Further

offshore this correlation often cannot be confirmed. As such, it is possible that the defined marine

landscapes harbour biological communities that are in reality not present. Therefore, usage of the

approach could be responsible for the inclusion of less reliable areas in the end product.

It needs emphasis that the marine landscape approach differs from general habitat mapping. Both

concepts make use of the same parameters (biotic and abiotic features), but especially the biotic

features are involved in a different way. However, differences in both approaches are clearly seen,

literature does not provide clear definitions of both terms. Especially, the meaning of the term ‘habitat’

seems to depend from one study to another, which makes interpretation more difficult. Therefore, it is

recommended to clearly outline what exactly is meant with the term habitat. Moreover; it would be

interesting to oppose both terms to clearly outline their differences as well as their similarities.

A Geographic Information System (GIS) is a powerful tool to capture, store, update, manipulate,

analyse and display all forms of geographically referenced information (Stanburry et al., 1999). A GIS

allows to perform many calculations and manipulations which generally lead to a solution, but it is

important to evaluate the outcome because some manipulations really lead to meaningless results.

Processing rough data also leads to information loss, which is often seen at the boundaries of datasets.

Datasets containing tiny fluctuations need to be handled with care in the purpose not to lose decisive

information.

The fact that many things can be done with datasets in general has implications towards the objectivity

of the end result. Once rough data are handled, the objectivity of the dataset is lost, especially when

the information of the datasets is put into a classification. GIS offers the possibility to test several

classifications and it is obvious that the best and most realistic classification will be chosen for further

processing. Besides, further processing of datasets by means of querying is anything but objective too.

However the aim of the marine landscape approach is to represent objective marine habitats, the

foregoing paragraph reveals that a 100 % objectivity is inevitably not obtained.

Discussion

35

In the framework of this study, it was chosen to work with a vector dataset. As seen in the map of the

seabed marine landscapes (Figure 6), features create definite and crisp boundaries and they are also

responsible for the development of slivers (paragraph 4.1). Therefore it was necessary to tidy up the

end result, which somehow again influences the objectivity of the marine landscape approach. The so-

called negligible polygons are adsorbed into neighbouring polygons, but in general slivers are tagged

with attributes. By adsorbing them into the neighbouring polygons, they suddenly belong to another

marine landscape characterized by other attributes.

Working with a raster data structure would not encounter such problems and the boundaries between

the different marine landscapes could be represented more fuzzy. However the end result might be

alike, it might be interesting to do the same process using a raster data structure. This would allow to

compare both results and might refute the argue that one data structure should be chosen above the

other.

The marine landscapes in this study are not compiled based on 100 % full coverage datasets. As such,

they are able to bias the end result in a significant way. The defined marine landscapes located at the

outmost northern part of the BCS are characterized by very little attributes due to absence of data.

Therefore it was not possible to make a prediction towards the biology. Besides, absence of biological

samples in that particular region does not allow to validate those landscapes. The defined seabed

marine landscapes are therefore biologically irrelevant and should be redefined in time when all

datasets show full coverage.

When seabed marine landscapes are determined it is important that they reflect as good as possible

what is present on the seabed. However a depth criterion is included in determining the marine

landscapes, it seems that this parameter is quite subjective. Including a depth criterion as such, gives

the idea that macrobenthic communities are restricted to one particular depth which is not the case.

After all, the BCS is not flat plane, but is characterized by a highly diverse topography. A criterion

such as the bathymetric position index (Weiss, 2001; Iampietro et al., 2002; Lundblad, in press.) as a

measure of where a location, with a defined depth is relative to the overall landscape, would be a more

objective.

The defined units are far from complete and it is recommended to refine the proposed ecological units

over time. The moderate correlation between the geophysical parameters and the biological

information could indicate that the criteria used to develop seabed marine landscapes are still too

rough and must be refined in the future to allow a better prediction of biological communities. It

would be interesting to include additional datasets containing information on silt-clay percentage,

currents, turbidity and sediment transport. It might also be valuable to include datasets on salinity and

seabed temperature as well, however the fluctuations of these parameters are considered to be less

Discussion

36

variable at the scale of the BCS. Besides, it is possible that other criteria, that are not known for so far,

have to be included in time. The more datasets that are used, the more difficult it will be to query the

datasets, but the more sophisticated and more realistic the defined marine landscapes will be.

As also mentioned by Golding et al. (2004), the marine landscape maps will certainly need refinement

when more biological information will come available over time.

However the organism-sediment relationship is emphasized, it is important not to exclude other

possible relationships and investigate whether other geophysical data beside the grain-size distribution

are interconnected with the macrobenthos. Possible relationships between dune fields, bed stress and

gravel fields on the one hand and the macrobenthos on the other hand were investigated, but it was not

possible to draw major conclusions so far. Further investigation would probably reveal an interrelation

between those variables.

In general, the approach offers quite potential towards the future. Generally, they can contribute in the

understanding of the marine ecosystem as such. The marine landscapes provide a scale at which the

vulnerability of the marine ecosystem to human activities can be assessed (Golding et al., 2004;

Vincent et al., 2004). When the marine landscapes will be refined in time, they can be used to deal

with protection, recovery and the sustainable use of the North sea.

Conclusions

37

7. CONCLUSIONS

The marine landscape approach allows to map habitats relatively fast without the usage of biological

data. The original concept was proposed by Roff and Taylor (2000) and in the framework of this thesis

applied to the Belgian continental shelf (BCS).

Based on available geophysical data, the BCS was divided into discrete ecological units. The

geophysical datasets made it possible to distinguish 17 seabed marine landscapes covering the whole

surface of the BCS. The distribution of those landscapes is shown in figure 6. The overall resolution of

the seabed marine landscapes is determined by the accuracy of the layer with the poorest resolution

and comes to 250 m.

Not every defined landscape covers a similar area: only four marine landscapes cover more than 50 %

of the BCS. This reflects and emphasizes that the BCS is mainly characterized by small-scale

differences.

Solely based on the geophysical characteristics a prediction was made towards the biology. When all

available and defined criteria are strictly applied, the possibility to predict macrobenthic communities

in the defined marine landscapes was rather poor and lying below 50 %. To improve the results, it was

necessary to interpret the defined criteria in a broader sense.

A validation process ascertains the biological relevance of each defined seabed marine landscape. To

investigate this relevance, two datasets were passively used: the first contained biological information

from samples, the latter from sediment parameters.

Generally, there is a quite good correlation between both dataset. However, the latter dataset is

strongly biased and shows in almost every marine landscape a dominance of the Nepthys cirrosa

community, the correlation reveals that the latter dataset offers a quite good alternative to validate the

seabed marine landscapes where real biological information is absent.

Furthermore, the distribution of the biological samples reveals that the marine landscapes generally

support different communities of species. The precise combination of species varies from one marine

landscape to another. This can be explained by means of particular environmental characteristics and

interactions between the several species.

Based on the available biological data it was not possible to validate each marine landscape in the

same way. Landscapes characterized by a lot of biological samples are well validated and reveal a high

biological relevance. Besides, others were characterized by less biological samples and therefore less

well-validated. However a resemblance is obtained for several landscapes, the reliability is lower

because the lower density (number of samples per km²). Finally, there are landscapes that are not

Conclusions

38

validated because no biological information was available. Those landscapes are situated in the

outmost northern part of the BCS.

The high resemblance seen in some landscapes, which are mostly situated relatively close to the coast,

highlights the fact there are clear indications that the assumption of Roff and Taylor (2000) indeed can

be confirmed. However, at this stage it is not possible nor impossible to confirm or contradict this

proposed assumption for the whole study area. Besides, it needs emphasis that the marine landscape

approach is not completely objective due to processing datasets.

To summarize, it may be said that based on the two datasets there was generally a moderate

correlation between the marine landscapes and the biological communities present on the BCS.

Inclusion of other datasets with a full coverage and more biological information would not only lead to

a refinement of the defined eco-units but would probably also increase the biological relevance of the

marine landscapes.

List of Tables

v

LIST OF TABLES

Table 1 Datatype and format of the available datasets ......................................................................................6

Table 2 Classification of the median grain-size dataset as used for this study (expressed in µm)...................11

Table 3 Summary of the physical characteristics of each seabed marine landscape type ................................18

Table 4 Extent of each seabed marine landscape. The values are ranked in a descending order .....................20

Table 5 Overview of the preferences of the biological communities present on the BCS ...............................23

Table 6 Summary of the calculation of the expected communities in each defined seabed marine

landscape...............................................................................................................................................................24

Table 7 Summary of the expected communities in each defined seabed marine landscape. The benthic

communities indicated in bold are predicted based on the grain-size criterion. The others represent predictions

when the criterion is more fuzzy interpreted. ......................................................................................................25

Table 8 Biological characterization of the defined seabed marine landscapes by means of biological data

derived from samples ...........................................................................................................................................29

Table 9 Biological characterization of the defined seabed marine landscapes by means of predictions from

sedimentological samples.....................................................................................................................................32

List of Figures

vi

LIST OF FIGURES

Figure 1 Processed bathymetry into depth classes.............................................................................................9

Figure 2 Processed slope dataset converted from raster to features..................................................................10

Figure 3 Median grain-size distribution, converted from raster to features......................................................11

Figure 4 Generalized bedforms based on a DTM of all single-beam registrations on the Belgian continental

shelf.......................................................................................................................................................................13

Figure 5 Maximum bed stress, converted from raster to features .....................................................................14

Figure 6 Illustration of the defined seabed marine landscapes. Each marine landscape is indicated with a code

...............................................................................................................................................................................16

Figure 7 Distribution of the biological samples taken at the BCS (based on Macrodat database, Ghent

University, Marine Biology Section) ...................................................................................................................26

Figure 8 Density classification showing the number of samples taken per km² (based on Macrodat database,

Ghent University, Marine Biology Section) ........................................................................................................27

Figure 9 Number of observations per marine landscape ...................................................................................28

Figure 10 Distribution of the biological communities based on sediment variables (median grain-size and silt-

clay percentage) ....................................................................................................................................................30

Figure 11 Number of observations per marine landscape based on sediment variables (median grain-size and

silt-clay percentage)..............................................................................................................................................31

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defining marine landscapes on the belgian continental shelf as

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