Exploring the dynamic between pollinators and conservation land. This is a new and reduced version of the processing in polinizadores.
- For each species, create a model that predicts likelihood of species occupancy based on the environment variables (altitude, landuse, WorldClim).
- Use each model to predict species occupancy across Mexico.
- Sum the likelihood rasters into a richness raster.
- species points from GBIF provided by Dr. Quesada
- predictor variables: all 19 WorldClim bioclimatic variables, elevation, land use/landcover, biomes
- administrative boundaries for context
- polygons of natural protected areas (areas protegidas naturales, ANPs)
- Mexico states: input_data/.../dest18gw.shp
- Stacked predictor variables with accompanying GRD file: all files in tidy/environment_variables
- If the above does not exist, then: all files in input_data/environment_variables/cropped
- species distribution models (SDMs) for all species
- dataframe with species modeling attributes
- richness layers by species groupings
- richness maps
- richness distribution by ANP zone, ecoregion, and pollinator group
- Modify parameters in
00_initialize.R
, such as parameters for the filtering of species observation points and parameters for the random forest model. - If the point data needs to be re-processed, run
prep_Quesada_GBIF_data.R
. The functionadd_taxon_info
requires manual input when taxize needs help in identifying the best match. - To rerun the model, run
process_SDM_for_cluster.R
. - To perform spot-check and sum the SDMs into richness layers, run
process_SDM.R
. - To perform analysis on the richness results, run
analyze_richness.R
.