a24cf2a4-b0b8-456c-b09e-0cf56225cc89 JNCC HabitatMapping@jncc.gov.uk 2020-01-15T12:12:52 ISO19115 2003/Cor.1:2006 WGS 84 (EPSG:4326) EPSG 7.9 Krieged BroadScale Substrate Map of Swallow Sands rMCZ GB400008 2014-03-25 2018-04-26 GB400008 EMODnetSeabedHabitats JNCC requested that BGS carry out geostatistical analysis of sediment sample data from the CEND 8/12 survey of Swallow Sand MCZ and CEND 6/13 survey of South-west Deeps (West) MCZ in order to produce maps of sediment distribution in the sites. For each of the MCZ a geostatistical analysis of the data is reported leading to the selection of a robust 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 back-transforming 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 underly the EUNIS habitat classification is found at the node. Defra open@defra.gsi.gov.uk JNCC habitatmapping@jncc.gov.uk Habitats and biotopes GEMET - INSPIRE themes, version 1.0 2008-06-01 geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme Eunis http://inspire.ec.europa.eu/codelist/ReferenceHabitatTypeSchemeValue 2018-04-16 ┬® Defra. Data may be re-used under the terms of the Open Government Licence. geossAttribution no limitation 100000 biota oceans -0.056792 -1.363764 55.473783 56.027671 2013-01-01 2013-12-31 LARK, R.M. 2014. .Mapping seabed sediments of the Swallow Sand and South-west Deeps (West) MCZs. British Geological Survey Internal Report, OR/14/015. 18pp. ESRI Shapefile 1.0 https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_view_maplibrary/wms? OGC:WMS gb400008 View map "GB400008" through the EMODnet Seabed Habitats "Map Library" Web Map Service https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_open_maplibrary/wfs? OGC:WFS gb400008 Access data from map "GB400008" through the EMODnet Seabed Habitats "Map Library" Web Feature Service https://files.emodnet-seabedhabitats.eu/data/EMODnetSBHsurvey_GB400008.zip WWW:LINK-1.0-http--link EMODnet Seabed Habitats download Corrigendum to INSPIRE Metadata Regulation published in the Official Journal of the European Union, L 328, page 83 2009-12-15 See the referenced specification true Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification true The data used are particle size analyses from the verification survey carried out by Cefas in 2012 (Ware, 2012). The data were collected by 0.1 m2 Hamon grab from 103 locations on a preplanned survey grid. Exploratory analysis of the data showed that sample stations 465 and 467 have duplicate coordinates. Because such duplicated data cannot be used in the methodology of Lark et al. (2012) these two observations were excluded, leaving 101 observations. This would generally be regarded as sufficient for spatial modelling (Webster and Oliver, 1992). All analyses were therefore completed on this data set, since it is preferable to use data which are collected on a common support (the size and shape of the sample volume) and produced by a consistent analytical methodology. 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). 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. 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. Further work is required on how conditional expectations and their uncertainty for compositional variables are most effectively expressed and communicated. 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 there 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.