56A8D063-0576-4376-97FF-1F21D5DA64E6
dataset
Dr. Kerry Howell
Plymouth University
kerry.howell@plymouth.ac.uk
2020-01-15T12:10:09
INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119
V. 1.2
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center
http://www.opengis.net/def/crs/EPSG/0/4258
EPSG
6.14(3.0.1)
Predicitive habitat model of Lophelia pertusa distribution in Hatton Bank and George Bligh Bank, UK
GB300001
2011-06-18
56A8D063-0576-4376-97FF-1F21D5DA64E6
Kerry L. Howell, Rebecca Holt, In├®s Pulido Endrino and Heather Stewart, When the species is also a habitat: Comparing the predictively modelled distributions of Lophelia pertusa and the reef habitat it forms, Biological Conservation, Volume 144, Issue 11, 2011, https://doi.org/10.1016/j.biocon.2011.07.025.
Predicitive habitat model of Lophelia pertusa distribution in Hatton Bank and George Bligh Bank
Kerry L.Howell, Rebecca Holt, In├®s Pulido Endrino, Heather Stewart
Dr. Kerry Howell
Plymouth University
kerry.howell@plymouth.ac.uk
Habitats and biotopes
GemetInspireTheme
GEMET - INSPIRE themes, version 1.0
2008-06-01
Downloadable Data
Viewable, not downloadable
biota
oceans
geoscientificInformation
Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.5.1.7333
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Unified Code of Units of Measure
32
Raster Dataset
https://www.emodnet-seabedhabitats.eu/access-data/launch-map-viewer/?zoom=7¢er=-17.641,58.544&layerIds=987&baseLayerId=-3&activeFilters=
WWW:LINK-1.0-http--link
EMODnet Seabed Habitats interactive map
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
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
true
This study uses Maxent predictive modelling to investigate whether the distribution of the species acts as a suitable proxy for the reef habitat. Models of both species and habitat distribution across Hatton Bank and George Bligh Bank are constructed using multibeam bathymetry, interpreted substrate and geomorphology layers, and derived layers of bathymetric position index (BPI), rugosity, slope and aspect. Species and reef presence records were obtained from video observations. For both models performance is fair to excellent assessed using AUC and additional threshold dependant metrics. 7.17% of the study area is predicted as highly suitable for the species presence while only 0.56% is suitable for reef presence, using the sensitivityÔÇôspecificity sum maximisation approach to determine the appropriate threshold. Substrate is the most important variable in the both models followed by geomorphology in the RD model and fine scale BPI in the SD model. The difference in the distributions of reef and species suggest that mapping efforts should focus on the habitat rather than the species at fine (100 m) scales.