Understanding the relationship between marine species and their environment is key to biodiversity research and conservation. For years, terrestrial scientists have relied on WorldClim for high-resolution environmental data. Now, marine researchers have their equivalent: Bio-ORACLE.
Originally launched in 2012, Bio-ORACLE provides crucial data on ecologically important ocean variables. Our latest update significantly expands this resource, incorporating:
- Future Climate Scenarios: The newest IPCC climate models, including Shared Socioeconomic Pathways, for long-term projections.
- Enhanced Data: New variables such as sea ice thickness, dissolved iron, phytoplankton, and light at the seafloor.
- Ocean Depths: Near-bottom data layers to model the rich biodiversity of deeper marine habitats. The importance of these additions is evident in the vast temperature differences between surface and bottom waters (sometimes exceeding 28°C!). We’ve rigorously tested our new data against quality-controlled sources, ensuring accuracy.
Easy to Use
Our accompanying R and Python packages offer easy access, management, and integration of Bio-ORACLE data into your existing research workflows.
Why This Matters
Bio-ORACLE is an indispensable tool for marine scientists, conservationists, and anyone interested in the ocean’s future. Use this powerful dataset in Species Distribution Modelling (SDM) to predict the distribution of species at the global scale, including non-native species, address niche-based questions and phylogeographic hypotheses, identify biodiversity hotspots and support the conservation and management of marine biodiversity. Moreover, the development of biologically meaningful variables for future climate change scenarios (e.g. dissolved oxygen, primary productivity and pH) allows more realistic estimates of the anthropogenic pressures that may lead to extinction and turnover of populations.
Rationale
Impacts of climate change on marine biodiversity are often projected with species distribution modelling using standardized data layers representing physical, chemical and biological conditions of the global ocean. Yet, the available data layers (1) have not been updated to incorporate data of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), which comprise the Shared Socioeconomic Pathway (SSP) scenarios; (2) consider a limited number of Earth System Models (ESMs), and (3) miss important variables expected to influence future biodiversity distributions. These limitations might undermine biodiversity impact assessments, by failing to integrate them within the context of the most up-to-date climate change projections, raising the uncertainty in estimates and misinterpreting the exposure of biodiversity to extreme conditions.