Frequently Asked Questions

(FAQ)

This is not an abnormal phenomenon and it is described in the literature for the first years of simulations. RCP scenarios only start showing contrasting outputs from ~2050 onwards. Moreover, RCP45 is projected to be less intense than RCP26 for that period at global scales.
By present we mean the long-term averages between 2000 and 2014.
We are not providing data per year yet. Bio-ORACLE aims for the broad community of ecological modellers and the data provided are easy to use averages of long term extremes. You may find these data in Copernicus.
We are not providing past data yet.
These are different prediction scenarios: B1 corresponds to the Special Report on Emissions Scenarios report, published by the Intergovernmental Panel on Climate Change (IPCC) and published in 2000. RCP4.5 refers to the Representative Concentration Pathways (RCP) adopted in the IPCC fifth Assessment Report in 2014.

We recommend to use the latest version of Bio-Oracle which is based on the Representative Concentration Pathways. We keep the previous versions available for reproducibility, but new research should use the most updated version.
Big lakes and the Caspian Sea are outside of the scope of bio-ORACLE. There is however a publication that covers these regions:
Lattuada, M., Wilke, T., and Raes, N.. 2020. Caspian Sea environmental variables: an extension of the Bio-ORACLE ocean data set. Ecology 101( 9):e03076. 10.1002/ecy.307610.1002/ecy.3076
The Bio-Oracle dataset is currently provided in two well-known raster standard formats: TIFF and ASC. They are compatible with most free open source GIS software: you can open it with QGIS , with the raster library in R or rasterio in Python. The Bio-Oracle dataset is also compatible with the licensed software ESRI ArcGIS.
The Bio-Oracle dataset is provided in raster files, which are georeferenced images. Each pixel of the image contains a value and it is located within a certain latitude and longitude. To open in a spreadsheet like Excel or Google spreadsheets you must convert the raster data into a data table format (tabuated txt or csv). This means you have to extract the value in each single pixel, along with the latitude and longitude of the center of the pixel. In R you can use the getValues function of the raster package.
Currently the best way to select the values of a given region is to mask it with a spatial polygon. The function mask (R software) from the package raster eliminates the values outside your region of interest. Then you just need to use the function getValues for the region.
There are two peer-reviewed publications:

Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F. and De Clerck, O. (2012), Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21: 272-281. https://doi.org/10.1111/j.1466-8238.2011.00656.x

Assis, J, Tyberghein, L, Bosch, S, Verbruggen, H, Serrão, EA, De Clerck, O. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecol Biogeogr. 2018; 27: 277– 284. https://doi.org/10.1111/geb.12693
Please cite our peer-reviewed publications:

Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F. and De Clerck, O. (2012), Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21: 272-281. https://doi.org/10.1111/j.1466-8238.2011.00656.x

Assis, J, Tyberghein, L, Bosch, S, Verbruggen, H, Serrão, EA, De Clerck, O. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecol Biogeogr. 2018; 27: 277– 284. https://doi.org/10.1111/geb.12693

Please also cite our R package sdmpredictors if you used it through your analysis:

Samuel Bosch (2020). sdmpredictors: Species Distribution Modelling Predictor Datasets. R package version 0.2.9. https://CRAN.R-project.org/package=sdmpredictors



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