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Code for large scale forecasting the potential distribution of Heracleum Sosnowskyi on the territory of Russia under climate change.

Source code for paper Large scale forecasting the potential distribution of Heracleum Sosnowskyi on the territory of Russia under the climate change

We propose a machine learning approach based on the Random Forest model for forecasting the potential distribution of Heracleum Sosnowskyi. This research aims to establish the possible habitat suitability of HS in current and future climate conditions across the territory of European part of Russia.

Map demonstrate points with Heracleum Sosnowskyi that were obtained from open sources

Occurence points

Installation

Clone this repository

Install R packages

  • biomod
  • spThin
  • biomod2
  • ggplot2
  • gridExtra
  • raster
  • rasterVis
  • maptools

Data

Occurrence points of Heracleum Sosnowskyi

The CSV file contains the coordinates of the location of the Heracleum Sosnowskyi and the parameters (soil variables, bioclim data) used to train the Random Forest model

CSV file: Occurrence points

Climatic variables

Climatic variables were collected from the Worldclim project

Soil data

Soil data were downloaded from the SoilGrids database

Source Code

Source code of paper to conduct Random Forect model training, reproduce results and plots contatins in src.R file - Code

Trained model stored in models folder - Model

Code to forecast future dictribution of Heracleum Sosnowskyi under different climate scenarios - Code

Plots

ROC-AUC, MDG and MDA plots created with python.

To reproduce plots install python packages

  • matplotlib
  • numpy
  • seaborn
  • sklearn
  • pandas
  • ipython
  • jupyter

Open ROC-AUC plots.ipynb file with Jupyter-notebook

License

Distributed under the CC0 1.0 license. See LICENSE for more information.