Having discussed with @SanderDevisscher and looked at the results of the bulbul climate matching, we notice that the maps produced are not very clear nor useful to provide an idea to a risk assessor about areas at risk. The reason is that the %overlap (perc_climate) is calculated on the entire global dataset and many records do not "hit" a climate classification that occurs in the risk assessment area (Europe). Consequently, when making the legend, the 'suitability' is extremely low overall.
To better visualize the areas at risk in Europe, we propose to calculate the % overlap using only the records that intersect in a climate classification occurring in Europe. Maybe we could also think about an automated way of reclassification (highly suitable, moderately suitable, low suitability).
Having discussed with @SanderDevisscher and looked at the results of the bulbul climate matching, we notice that the maps produced are not very clear nor useful to provide an idea to a risk assessor about areas at risk. The reason is that the %overlap (perc_climate) is calculated on the entire global dataset and many records do not "hit" a climate classification that occurs in the risk assessment area (Europe). Consequently, when making the legend, the 'suitability' is extremely low overall.
To better visualize the areas at risk in Europe, we propose to calculate the % overlap using only the records that intersect in a climate classification occurring in Europe. Maybe we could also think about an automated way of reclassification (highly suitable, moderately suitable, low suitability).