check if function dismo:evaluate() works for all types of fitted models. Or if we need our own.

• For tree data in all NFIs; e.g. wzp data table in German NFI

• For other data, e.g. "jung"

• Collaborate with https://github.com/EhrmannS/lomm R-package project?

Add function argument for growth thresholds. Add function for calculation of growth thresholds analogous to sdm.

Code is in project priority_regions. Needs to be changed so that data can be provided in the package.

Is there a special reason, why we use resample() instead of aggregate() to change the resolution of the resulting raster?

resample() uses bilinear interpolation, which is in my opinion not totally appropriate here. aggregate() offers the possibility to use different functions to calculate the new cell values.

The resulting rasters do not only differ in cell values but also in the number of assigned cells (NA vs. values; visible discrepancy for 7 km resolution).

L. 133 of /R/site_suitability.R:
species_priority <- resample(species_priority, raster(ext=extent(species_priority), resolution=resolution))
why not
species_priority <- aggregate(species_priority, fact=7, fun=mean)?

I produced two example plots (Picea abies).
aggregate:

resample:

... depending on sampling density as a simply way to consider differences in sampling design.
Consider additionally Germany nested design

Add solution for pos change of one variable and negative change for the other! Results in pos or neg change?

Could you please check commit fe7f182.
Was it correct to change growth_data to gdata in function create_basal_area_data?

Could you also please check function create_species_priority_regions:
argument names in () do not match names in {}:
create_species_priority_regions <- function(basal_area_data, species="Picea_abies", hti_change, resolution=7000, negative_only=FALSE){
...
badata_species <- badata[badata$species==species,]
...
}
Do we use basal_area_data or badata?