Predicitive habitat model of Lophelia pertusa distribution in Hatton Bank and George Bligh Bank, UK
Predicitive habitat model of Lophelia pertusa distribution in Hatton Bank and George Bligh Bank
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Alternate title | GB300001 |
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Date | 2011-06-18 |
Date type | Creation: Date identifies when the resource was brought into existence |
Unique resource identifier | 56A8D063-0576-4376-97FF-1F21D5DA64E6 |
Credit | Kerry L.Howell, Rebecca Holt, In├®s Pulido Endrino, Heather Stewart |
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Point of contact
Individual name | Dr. Kerry Howell |
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Organisation name | Plymouth University |
Electronic mail address | kerry.howell@plymouth.ac.uk |
Role | Point of contact: Party who can be contacted for acquiring knowledge about or acquisition of the resource |
Maintenance and update frequency | Irregular: Data is updated in intervals that are uneven in duration |
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Descriptive keywords
GemetInspireTheme | Habitats and biotopes |
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GDI-Vlaanderen Trefwoorden | Metadata GDI-Vl-conform |
Other keywords | Downloadable Data |
Spatial representation type | Grid: Grid data is used to represent geographic data |
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Language | English |
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Character set | UTF8: 8-bit variable size UCS Transfer Format, based on ISO/IEC 10646 |
Topic category code |
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Environment description | Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.5.1.7333 |
Reference System Information
Unique resource identifier | http://www.opengis.net/def/crs/EPSG/0/4258 |
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Codespace | EPSG |
Hierarchy level | Dataset: Information applies to the dataset |
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Lineage
Statement | 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. |
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Domain consistency
Conformance result
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mdLegalAndSecurityConstraintsSection
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Distribution format | Raster Dataset | ||||||||
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File identifier | 56A8D063-0576-4376-97FF-1F21D5DA64E6 | ||||||||
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Metadata language | English | ||||||||
Character set | UTF8: 8-bit variable size UCS Transfer Format, based on ISO/IEC 10646 | ||||||||
Hierarchy level | Dataset: Information applies to the dataset | ||||||||
Hierarchy level name | dataset | ||||||||
Date stamp | 2020-01-15T12:10:09 | ||||||||
Metadata standard name | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 | ||||||||
Metadata standard version | V. 1.2 | ||||||||
Contact
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Overviews
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Associated resources
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