Identifying science-policy consensus regions of high biodiversity value and institutional recognition

We retrieved 63 articles presenting prioritization maps (out of 5137 screened) and grouped these into three separate clusters based on their underlying methodology and input data, using Multivariate Component Analysis and Hierarchical Clustering on Principal Components. By combining these maps, weighted according to their cluster characteristics, we generated a map of scientific-consensus regions with the highest overlap of independently generated biodiversity priorities. We also created a map of policy-consensus, representing regions with the highest potential to attract the interest of the international conservation organization.

Here we made available the raster version of  of scientific-consensus regions and the map of policy-consensus.

The maps are two raster of continuos value, from a minimum of 0 to a max which depend on the number of overlapping priority conservation maps

Detailed methods are available from Cimatti et al. (2021) https://doi.org/10.1016/j.gecco.2021.e01938

In the zip folder you can find scientific-consensus  of policy-consensus maps with original values and reclassified with percenitles