Seabed sediment mapping is important for a wide range of marine policy,
planning and scientific issues, and there has been considerable national and
international investment around the world in the collation and synthesis of
sediment datasets. However, in Europe at least, much of this effort has been
directed towards seabed classification and mapping of discrete habitats.
Scientific users often have to resort to reverse engineering these
classifications to recover continuous variables, such as mud content and
median grain size, that are required for many ecological and biophysical
studies. Here we present a new set of 0.125
Knowledge of the geographic variation in the sedimentary environment of the
seabed is required for a wide variety of marine planning and science tasks.
Benthic species have differing sediment requirements and seabed mapping can
therefore help identify ecologically distinct habitats
The north-west European Shelf is one of the world's sea regions most impacted
by human activities
This study was motivated by the need for openly available datasets of the
sedimentary environment for parameterizing shelf sea ecosystem models (e.g.
A key challenge to mapping seabed sediments across the north-west European
Shelf is that sediment data are unavailable across the entire region. In areas
with high-quality spatial sediment data, it is relatively easy to provide
credible maps of sediment composition using statistical interpolation
techniques. However, an alternative method is needed where there is poor or
no data coverage. Recently,
We extend this method by predicting the sediment composition of the seabed across
the entire north-west European Shelf. However, our approach to mapping differs
from that taken by
The motivation for the choice of seabed parameters is as follows. Mud, sand
and gravel percentages and rock cover are key determinants of the
suitability of a habitat for benthic species
Data products created at a spatial resolution of 0.125
Summary of data sources used in sediment analysis. Datasets 1
(
Region where the sedimentary environment was mapped. We defined the
north-west European Shelf as the region between 17
Our goal was to produce synthesized maps of the sedimentary environment of
the north-west European Shelf, which we define to be areas shallower than
500
Locations with field estimates of each seabed sediment parameter.
Data sources are listed in Table
Seabed sample coverage of this shelf region is highly heterogeneous with
large expanses of the domain lacking accessible data. Hence, our strategy was
to fill these voids in the sample coverage with statistically modelled
values. The steps involved in mapping the sedimentary environment were
therefore as follows.
Sediment data from a number of sources (Table In areas where we have data, we spatially interpolate the relevant statistic onto the study grid. Using observations, we developed random forest (RF) models to predict sediment
composition using wave and tidal velocities, bathymetric properties of the seabed and distance from the coast. We then used RF-predicted values to infill regions of the mapping domain
where the observed data density was insufficient for direct gridding. Sediment porosity and permeability at each map grid point were derived
from the whole-sediment median grain size using empirically based relationships assembled from literature data. The natural disturbance rates of sediments at each gridded location were then
calculated from wave and current bed shear stress and grain size estimates using sediment dislocation theory.
We compiled data on the sediment composition of the seabed from a large
number of sources. Our analysis uses the following data: mud, sand and
gravel percentages, rock cover and the median grain size of the
whole sediment, sand fraction and gravel fraction. The data sources are
summarized in Table
The British Geological Survey
An extensive dataset of surface mud, sand and gravel percentages was
compiled for the transnational database of North Sea sediments
Records of whole-sediment median grain size were available from the North Sea
Benthos Survey (NSBS)
The Centre for Environment, Fisheries and Aquaculture Science (Cefas) provided sediment data which included the mud, sand and gravel percentages and the distribution of sediments by grain size. Data provided by Cefas covered a large part of English and Welsh waters. In total, Cefas provided 3814 records of mud, sand and gravel percentages and sediment distribution. However, to provide a consistent estimate of sediment type we restricted our analysis to sediments analysed using laser methodology and from the top 10 cm of the seabed. This resulted in a total of 1879 sediment records being used. Cefas did not provide estimates of median grain size. We therefore calculated the median grain size as follows. For the sediment record at each location, a cumulative curve of sediment weight percentage was calculated. We then calculated the median point of this curve and classified this as the median grain size. This was carried out for the entire sediment and also for the gravel fraction.
Two datasets were provided by Marine Scotland. The first included mud, sand
and gravel percentages and whole-sediment median grain sizes for a large part
of the North Sea. In total, this dataset had 1214 sediment records. The
second dataset included estimates of the median grain size of the combined
mud and sand fraction. These grain size data were not directly usable, so we
filed out samples in which the percentage of gravel was small enough that the
median grain size of the mud–sand fraction was close to that of the whole
sediment. To do this we analysed Cefas data and established that when the
whole-sediment
The Infomar project (
Our aim was to classify locations as non-rock, rock at surface (i.e.
approximately the top 10
The British Geological Survey provides a database of downloadable historical
logs of sediment sampling surveys (available from
Before analysing the PDFs we created the following categories for the
records: (1) evidence shows there is no rock at the location; (2) written logs
are consistent with rock at the surface or rock covered by a thin skin of
sediment (approximately 10
Borehole records provide reliable records of the rock composition of the
seabed and the layers below it. German borehole data are available from the
Geopotenzial Deutsche Nordsee project. The
Depth-averaged tidal velocities were calculated as follows.
For most of the study region tidal velocities were taken from the output of the
Scottish Shelf Model, which is an implementation of the unstructured,
finite-volume 3-D hydrodynamic model FVCOM. The spatial domain of this model
covers approximately 80
For the rest of the model domain we derived tidal velocities as follows. The
Oregon State University Tidal Prediction Software (OTPS) is a well-known open-source barotropic tidal model based on the Oregon State University tidal
inversion of TOPEX/POSEIDON altimeter data and tide gauge data
Wave conditions were acquired from the ERA-Interim reanalysis
To calculate the bed shear stress we used the equations of
For the statistical modelling of sediment composition we used EMODnet
bathymetry data. These have a spatial resolution of 1/8
The synthetic maps of mud, sand and gravel percentages, and rock cover were created as follows. First we identified regions where a statistical interpolation of the relevant parameter would give a reasonable estimate across that region. In other regions we used statistical models to predict the parameter. We assume that the environmental drivers of sediment composition are consistent across space.
Sampling coverage of sediment composition covered almost all of the North
Sea, the United Kingdom's territorial waters and parts of Ireland's
territorial waters (Fig.
For areas outside the alphahulls we used random forest
The observed mud, sand and gravel percentages summed to 100. However, there
is no guarantee that separately predicted mud, sand and gravel percentages will
sum to 100. We therefore predicted the mud, sand and gravel percentages
separately and then a multiplier was applied to each prediction so that the
predictions were adjusted to total 100. Random forests were created in R
using the ranger package
A similar process was carried out for median grain sizes and carbon and
nitrogen content. Grain size data were available for large parts of the United
Kingdom's territorial waters and some parts of the North Sea, while carbon
and nitrogen content were exclusively available in parts of the United
Kingdom's territorial waters (Fig.
Predictors used for statistical models for predicting sediment parameters. When mud, sand and gravel percentages and whole-sediment median grain sizes were used as predictors, raw field data were used in the creation of the statistical models, whereas the synthetic maps created in this study were used for model predictions.
The environmental predictors used for the random forest models that predicted
mud, sand and gravel percentage, rock cover, and carbon and nitrogen content
are listed in Table
Tidal and wave energy levels at the seabed should strongly influence mud,
sand and gravel percentages. Large grain sizes require more energy to
dislodge from the seabed, and therefore high bed shear stress is associated
with increases in average grain size and reductions in mud content
Smoothness of the seabed will influence seabed disturbance and sediment
accumulation and is likely an indicator of the existence of rocky outcrops.
We therefore included measures of seabed roughness as predictors in each
random forest. A number of methods exist to quantify the roughness of the
seabed
The above predictors were used for the mud, sand and gravel percentage and
rock cover models. For the models of carbon and nitrogen content we also
included chlorophyll, salinity and seabed temperature. Carbon and nitrogen
content are influenced by biological activity and should thus be influenced
by primary production levels and temperature at the seabed. The
MetO-NWS-REAN-PHYS-bed-daily reanalysis was used for seabed temperature.
These data were downloaded from the Copernicus Marine Environmental Monitoring
Service website
(
Our methodology involves predicting the sedimentary environment in
geographically distinct regions. We therefore tested the ability of random
forest models to do this credibly by using a cross-validation technique
involving spatially disaggregated training and test datasets. Spatial
disaggregation has been shown to be a reasonable method to avoid the
excessive overconfidence that can possibly result from other training and
testing methodologies of spatial models
The relationship between mud estimated from laser and sieve
methodology for the same samples. For estimates of carbon and nitrogen
content with only sieve-based estimates of mud content, we estimated what the mud
percentage would be when calculated using laser methodology. The dashed red
line shows this relationship (laser mud
Sufficient median grain size data were available to provide a spatial
interpolation of whole-sediment
In contrast to the mud, sand and gravel percentages, we chose not to predict median grain sizes using environmental variables. Predicting both the sediment percentages and median grain sizes separately is likely to result in contradictory predictions. For example, a model might predict a much higher median grain size than is possible given the predicted sediment percentages. We therefore chose to create a statistical model which predicts the median grain size using mud, sand and gravel percentages.
The median grain size of the gravel fraction has previously been shown to
relate strongly to the mud to sand ratio
The median grain size of the whole sediment varied by 4 orders of magnitude.
Consequently, a GAM which uses the
For the sand and gravel fractions we used a GAM of the form
Published literature with porosity estimates. These data were used to statistically model porosity in terms of whole-sediment median grain size.
The porosity and permeability of sediments are quantitatively related to grain
size distribution, with coarser-grained sediments having lower porosity and
higher permeability. We evaluated the relationship between porosity and
whole-sediment median grain size by compiling published data
(Table
Fitted values and standard errors of the four parameters required for the function relating sediment porosity to median grain size.
We then used the porosity estimates and the maps of POC and TN to derive
additional maps of the density of carbon (
We modelled the extent to which the surface layers of the sediment were
disturbed by waves and tides during the year. Disturbance was defined as an
event which results in physical movement of the surface sediments due to the
effects of bed shear stress. We then estimated the average percentage of area
disturbed per month in each 0.125
Disturbance could be heterogeneous in space and time within each of our
0.125
We therefore estimate natural disturbance using the following procedure for each day of the
year.
Calculate the bed shear stress at each 15 Determine the critical threshold at each time step for mud, sand and gravel using the respective Percentage of area disturbed
We follow
The derivation of the synthetic map of sediment percentages. The interpolated map uses bilinear spline interpolation using sediment data over the region. The random forest map predicts the sediment percentages using a random forest model which relates the percentage to the bed shear stress and the distance to the coast. The synthesized map merges the two by using spatial interpolations where we have data and the random forest predictions where we do not.
Figure
The predictions of the random forest models reproduce the large-scale
geographic patterns of sediment composition. The
The GAM of whole-sediment
Figure
Figure
Summary of the derivation of the synthetic median grain size maps. Where
we have sufficient median grain size data we spatially interpolated a map of
Maps of porosity and permeability. The relationship between porosity and permeability and median grain size was estimated using published field data. We then predicted porosity and permeability using the synthetic map of median grain size.
Proportion of area in each rock classification. Areas were
classified by whether there was rock at the surface or a surface sediment
layer plus rock in the top 50
Derivation of the synthetic maps of particulate organic carbon (POC) and nitrogen (TN). Data were interpolated based on field observations in areas with good spatial coverage. In other regions, parameters were predicted using a random forest which had mud content and physical environmental variables as predictors.
Modelled monthly disturbance rate. A disturbance event was defined as a time when the bed shear stress exceeded the threshold required to move either the mud, sand or gravel portion of the sediment. The monthly disturbance rate was defined as the mean fraction of the total mud, sand and gravel area disturbed per day.
The synthetic maps of rock cover are shown in Fig.
The mapped carbon and nitrogen content of sediment are shown in
Fig.
Figure
The data products listed in Table
The underlying goal of this study was to synthesize large-scale information about the physical environment of the seabed, both in terms of the characteristics of sediment and the wave and tidal regimes which cause disturbance. Using field estimates of the sediment composition of the seabed, we were able to map with high confidence the sediment composition of the North Sea and British territorial waters, and we were able to make credible statistical predictions of the sediment composition in other regions. The compiled datasets of sediment composition and disturbance regime are, as far as we know, the most extensive that exist over such a large spatial scale. A number of applications exist for these datasets, including habitat mapping and quantification of anthropogenic disturbance on the seabed.
Habitat mapping requires knowledge of the composition of seabed sediments
A simplifying assumption of our study was that sedimentary environments are
in a state of equilibrium or near equilibrium throughout the European Shelf.
However, this is unlikely to be true everywhere.
Our maps of rock area are broadly comparable with the existing hard substrate
map for British territorial waters produced by the British Geological Survey
The confidence in our rock data products is significantly lower than that
for mud, sand and gravel percentages. However, this was an expected result
and was consistent with existing work
We excluded the influence of rivers from predictive models because of a lack
of large-scale data. However, it is likely that this is a key influence near
large estuaries. This can be seen in the high-energy Bristol Channel, where
there is both a high level of rock and a relatively high level of mud due to
the contradictory influences of strong tidal currents and the sediment
deposits from the river Severn
Previously,
The bed shear stress and sediment dislocation rates were calculated by
combining the equations of
Bed shear stress was calculated using the equations of
We calculate the wave orbital velocities using the equations of
The the zero-crossing period
Calculation inputs.
Calculation outputs.
We must then calculate a number of intermediate terms for the shear stress calculation.
We relate the kinematic viscosity
The current-only shear stress is calculated as follows.
When
We calculate wave-only stress as follows.
First we calculate the critical current Reynolds for transition from laminar to turbulent
flow.
We must recalculate
We calculate
We calculate the bed shear velocity as
The root mean square shear stress is calculated as follows:
High-resolution versions of the paper's figures have been made available as a supplementary file so that figures can be used in presentations and reports.
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
We thank John Aldridge (Cefas), Simon Greenstreet (Marine Scotland), Mike Robertson (Marine Scotland) and Jennifer Valerius (Federal Maritime and Hydrographic Agency, Germany) for providing access to sediment data. We are grateful to Michela De Dominicis and Judith Wolf (National Oceanography Centre, Liverpool) for providing outputs from the Scottish Shelf Model. British Geological Survey data were provided under Open Government Licence (contains British Geological Survey materials ©NERC). Valuable technical support was provided by Ian Thurlbeck. This paper received funding under the NERC Marine Ecosystem Programme (NE/L003120/1) and from the EPSRC TeraWatt and EcoWatt projects (EP/J010170/1 and EP/K012851/1). Edited by: Giuseppe M. R. Manzella Reviewed by: two anonymous referees