This repository was created as a supporting material for the article exploring the trade-offs between positional error and analysis grain in species distribution modeling.
Code author: Lukas Gabor
Date: 01/12/2022
Relevant paper:
Gábor, L., Jetz, W., Lu, M., Rocchini, D., Cord, A., Malavasi, M., ... & Moudrý, V. (2022). Positional errors in species distribution modelling are not overcome by the coarser grains of analysis. Methods in Ecology and Evolution.
File Description:
R_Script: Step-by-step guideline for generating virtual species, sampling occurrence data and simulating positional error.
Note that we can not provide the LiDAR data, we used for generating virtual species, because they are owned by Krkonose Mountains National Park (available upon request for research purposes). Thus, we created a vs object that can be used to recreate the data. The vs object can be downloaded here.