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Title:
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)
Document owner:
Kristien Schelfaut ([email protected])
Els Verfaillie ([email protected])
Vera Van Lancker ([email protected])
Reviewed by:
Isabelle Du Four (UGent)
Workgroup:
MESH action:
4
Version:
1.1
Date published:
Language:
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
File name:
www.searchmesh.net
Reference/citation:
Keywords:
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.
Change history
Version:
Date:
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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.
4
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
5
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
6
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)
7
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.
8
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.
9
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).
10
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.
11
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.
12
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.
13
Figure 7 Map of the defined seabed marine landscape types on the Belgian Continental Shelf
14
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
15
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
17
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
19
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
21
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).
22
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).
23
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’.
24
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).
25
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.
26
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.
27
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
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