Integrated Marine Information System (IMIS)
Persons | Institutes | Publications | Projects | Datasets[ report an error in this record ] | basket (0): add | show |
Bio-ORACLE: a global environmental dataset for marine species distribution modelling Tyberghein, L.; Verbruggen, H.; Pauly, K.; Troupin, C.; De Clerck, O. (2012). Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Glob. Ecol. Biogeogr. 21(2): 272-281. https://dx.doi.org/10.1111/j.1466-8238.2011.00656.x
In: Global Ecology and Biogeography. Blackwell Science: Oxford. ISSN 1466-822X; e-ISSN 1466-8238
|
Available in | Authors |
Keywords |
Distribution Ecological niches Modelling Taxa > Species Codium fragile (Suringar) Hariot, 1889 [WoRMS] Marine/Coastal |
Author keywords |
|
Authors | Top | |
|
Abstract |
Location Global, marine. Methods We compiled global coverage data, e.g. satellite-based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio-ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user-friendly data package for marine species distribution modelling is available for download at http://www.bio-oracle.ugent.be. The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow-water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence-only model of a notorious invasive seaweed shows that the information contained in Bio-ORACLE can be informative about marine distributions and permits building highly accurate species distribution models. |
Top | Authors |