Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

By Tyberghein Lennert, Heroen Verbruggen, K. Pauly, C. Troupin, F. Mineur, O. Clerck. 2012

856 citations recorded by the source corpus.

Subfields: Ecological Modeling, Oceanography

View Paper: https://www.semanticscholar.org/paper/ee1211a7808fd2746a8469c4be1c899aaf349019

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Citations in the corpus

Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (Diva) (2012, methodology): “Global analysis at the ocean surface were generated by Tyberghein et al. (2012) for various physical and chemical variables.”
Benthos distribution modelling and its relevance for marine ecosystem management (2015, background): “Initiatives such as Bio-ORACLE (Tyberghein et al., 2012), MyOcean (Buongiorno Nardelli et al.”
Modelling of European hake nurseries in the Mediterranean Sea: An ecological niche approach (2015, background): “…Species distribution modelling (SDM) (Elith and Leathwick, 2009; Guisan and Thuiller, 2005; Peterson and Soberón, 2012) have become increasingly popular tools in the study of marine species distribution (Bentlage et al., 2013; Friedlaender et al., 2011; Tyberghein et al., 2012; Wiley et al., 2003).”
Will remote sensing shape the next generation of species distribution models? (2015, background): “…at http://www.bio-oracle.ugent.be), a marine counterpart of the WorldClim database has been developed, consisting of 23 environmental rasters, derived from both satellite-based and in situ data for modeling the distribution of shallow water marine species at a global scale (Tyberghein et al. 2012).”
ntbox: An r package with graphical user interface for modelling and evaluating multidimensional ecological niches (2020, positive): “…the basic operations that the ntbox package supports are: (a) obtaining environmental data through WorldClim (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), CHELSA (Karger et al., 2016, 2017), Bio-Oracle (Assis et al., 2018; Tyberghein et al., 2012) and ENVIREM (Title & Bemmels, 2018); ( , 2015).”
The ‘golden kelp’ Laminaria ochroleuca under global change: Integrating multiple eco‐physiological responses with species distribution models (2018, methodology): “The A2 scenario, with temperature increases of 2.6°C and CO2 emissions >800 ppm until 2100, was the most severe among those provided by Bio- Oracle (Meehl et al., 2007).”
Climate Change Impacts on Seagrass Meadows and Macroalgal Forests: An Integrative Perspective on Acclimation and Adaptation Potential (2018, background): “Based on high-quality data retrieved from comprehensive environmental datasets (van Vuuren et al., 2011; Taylor et al., 2012; Tyberghein et al., 2012; Assis et al., 2017a) and empirical approaches (Franco et al.”
Predicting distributions of invasive species (2013, background): “…variability and climate close to the ground are being prepared globally, some at fine resolution (B. McGill & R. Guralnick, personal communication, 2012; Kearney et al. 2014), and coarse resolution marine datasets are now available with a suite of useful predictors (e.g. Tyberghein et al., 2012).”
Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers (2017, methodology): “Climatologies are often derived from satellite images (Tyberghein et al., 2012) or summarizations of in situ databases (e.g., the World Ocean Atlas (Levitus et al., 2013) and the CSIRO Atlas of Regional Seas (Ridgway, Dunn, & Wilkin, 2002)).”