Krieged BroadScale Substrate Map of Fulmar rMCZ
A geostatistical analysis of the data is reported leading to the selection of a linear model of corregionalization for the composition of the sediment, based on the additive log-ratio transformation of data on mud, sand and gravel content. This model is then used for spatial prediction on a 250-m grid. At each grid node a prediction distribution is obtained, conditional on neighbouring data and the selected model. By sampling from this distribution, and backtransforming onto the original compositional simplex of the data, we obtain a conditional expectation for the proportions of sand, gravel and mud at each location, a 95% confidence interval for the value at each node, and the probability that each of the four sediment texture classes that underlie the EUNIS habitat classification is found at the node.
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Alternate title | GB400007 |
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Date | 2015-03-30 |
Date type | Creation: Date identifies when the resource was brought into existence |
Date | 2018-04-26 |
Date type | Publication: Date identifies when the resource was issued |
Unique resource identifier | GB400007 |
mdConstraintsSection
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Point of contact
Organisation name | Defra |
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Electronic mail address | open@defra.gsi.gov.uk |
Role | Owner: Party that owns the resource |
Point of contact
Organisation name | JNCC |
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Electronic mail address | habitatmapping@jncc.gov.uk |
Role | Custodian: Party that accepts accountability and responsability for the data and ensures appropriate care and maintenance of the resource |
Descriptive keywords
GEMET - INSPIRE themes, version 1.0 | |
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http://inspire.ec.europa.eu/codelist/ReferenceHabitatTypeSchemeValue | Eunis |
GDI-Vlaanderen Trefwoorden | Metadata GDI-Vl-conform |
Spatial representation type | Vector: Vector data is used to represent geographic data |
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Spatial resolution
Denominator | 100000 |
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Language | English |
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Topic category code |
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Supplemental Information | Lark, R.M.. 2015 Mapping seabed sediments of the Fulmar rMCZ. Nottingham, UK, British Geological Survey, 12pp. (OR/15/015) (Unpublished) Available from: http://nora.nerc.ac.uk/id/eprint/510587/ |
Reference System Information
Unique resource identifier | WGS 84 (EPSG:4326) |
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Codespace | EPSG |
Hierarchy level | Dataset: Information applies to the dataset |
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Lineage
Statement | The data used in this work were obtained by Cefas in 2012 (Ware & Meadows, 2013). A total of 65 stations were sampled by 0.1 m2 Hamon grab on a pre-planned survey grid. JNCC required that only PSA data supplied by JNCC specifically for this work should be used to produce the outputs. This is to remove compatibility issues arising from the use of PSA data originating from differing grab techniques. The data provided are percent by mass of gravel (particles diameter > 2mm), mud (particles diameter < 0.063 mm) and sand (particles 2mm > diameter > 0.063 mm). Before modelling the linear coregionalization, exploratory spatial analysis was undertaken. There is a pronounced cluster of observations in the south west corner of the rMCZ, and the exploratory analysis indicated that this clustering was a potential source of artefacts for spatial modelling. Whilst some close-spaced observations are desirable for the estimation of spatial models, this pronounced clustering can cause problems because of over-representation of one part of the region (e.g. Marchant et al., 2013). For this reason the cluster of points was removed leaving 52 data for spatial modelling. All 65 data were used subsequently for mapping, as described in section 2.3. Auto-variograms and cross-variograms of the alr-transformed data were estimated from the data. The same method-of-moments estimator (MoM) was used as described by Lark et al. (2012). The linear model of coregionalization (LMCR) was fitted to the MoM estimates of the auto- and cross-variograms by weighted least squares, as described by Lark et al. (2012). Lark et al. (2012) describe the cokriging procedure used to obtain conditional expectations of the transformed variables and covariance matrices for these at target points. This procedure was undertaken to form predictions at nodes of a 250-m grid. At this stage all 65 observations in the data set were used. The simulation method used by Lark et al. (2012) was then used to generate 5000 independent realizations from the joint prediction distribution at each node. For each realization a back-transformation was undertaken to give values of gravel, mud and sand. Over all realizations the mean value of gravel, mud and sand were computed as the conditional expectation of these variables, and the 0.025 and 0.975 quantiles of the realizations were computed as confidence intervals for the predictions. It should be noted that these predictions and confidence intervals should be considered for each variable in turn. For each realization, the EUNIS level 3 sediment texture classes (Long, 2006) were identified. At each grid node the proportion of realizations that occurred in each class is an estimate of the conditional probability of finding that class at the location. One may report the probability for each class, one may also report the class of maximum probability. The uncertainty attached to treating a site as if the class of maximum probability were the true class, can be evaluated by examining that maximum probability value, which may range from just over 1/k (where k is the number of classes) to 1.0. |
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Domain consistency
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Distribution format | ESRI Shapefile (Version: 1.0) | ||||||||||||||||||||||||||||||||||||||
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File identifier | 5e247290-6076-48d4-9fe3-93a097f4ae83 | ||||||
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Metadata language | English | ||||||
Character set | MD_CharacterSetCode_utf8: MD_CharacterSetCode_utf8 | ||||||
Hierarchy level | Dataset: Information applies to the dataset | ||||||
Date stamp | 2020-01-15T12:07:36 | ||||||
Metadata standard name | ISO19115 | ||||||
Metadata standard version | 2003/Cor.1:2006 | ||||||
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