This is a simple Predicted Niche Occupancy (PNO) calculator that is intended to be comparable to but faster and more flexible than the implementation in R package phyloclim, function pno, for global-scale datasets at high resolution. A PNO is a method of extracting climatic tolerances directly from niche models, expressed in terms of probability of suitability over a series of environmental bins, and is commonly used in ancestral niche reconstruction and other applications.
This script is improved over previous implementations by:
- Improved spatial statistic efficiency.
- Using .tif rather than .asc for initial processing steps.
- Not depending on matching extent between the model and the environmental data -- especially useful where common bins are needed for global extents.
Run like so:
bash extract_and_join_climatesuitability.sh path_to_model_folder path_to_environmental_data_folder path_to_extraction_data number_of_bins
Common projections among models and environmental data (but NOT necessarily extents) are assumed. Missing data is assumed to be -9999. PNO results are encoded as a csv matrix with rows as species and columns as left-hand histogram bin boundaries; cells are bin probabilities. A set of sed commands at the end are intended to remove non-species designations in species names (variable names, etc.); edit as needed.
Two modules at the end, binner_climateextraction.py and bin_trimmer.py are optional. The former adds point extraction data to an existing PNO file (e.g., for species with too few occurrences to model directly; example input in the folder) and the latter drops columns with low probability for all species (hard coded as <1e-4).
Note that histograms are normalized but can sum to a number slightly different from one at ~15 decimal places due to float precision limitations. A correction step may be needed for applications that require summing to one.
Future speed improvements could include skipping the environmental extent-cropping step and only loading environmental data a single time for the spatial join operation across species.
- GDAL library executables in path:
gdal_translate,gdalwarp,gdaltindex. - Python libraries
numpy,pandas.