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# Species Distribution Modeling applied to _Aedes albopictus_ breeding sites in public spaces (Montpellier) and generating an environmental bias map
This project contains the necessary scripts to perform :
- Species Distribution Modeling (SDM) using biomod2 package in R with environmental variables and breeding sites data of _Aedes albopictus_. This script allows the calculation of presence probabilities.
- Exploration of environmental biases between breeding sites data and environmental variables.
## Installation
Before running the scripts, make sure you have the following R packages installed:
install.packages(c("biomod2", "raster", "rgdal", "sp", "ggplot2", "FactorMineR", "terra", "dplyr","sf"))
This script processes breeding site presence data (.csv) and environmental variables (.tif raster files) to model the distribution of Aedes albopictus breeding sites in public spaces. The workflow includes:
1. Loading Data: Import presence data and raster layers of environmental variables.
3. Running Individual Models: Train different SDM algorithms and evaluate their performance.
4. Assessing Model Performance: Extract evaluation scores and variable importance scores.
5. Plotting Response Curves: Visualize the relationships between environmental variables and the species presence.
6. Ensemble Modeling: Combine multiple individual models to improve prediction accuracy.
7. Projection: Apply the final model to the entire study area to generate probability maps of breeding site presence.
This script analyzes potential sampling biases by comparing the spatial distribution of presence data with the underlying environmental variables. It includes:
2. Extraction Environmental Characteristics : Extract environmental variables at breeding site locations from the environmental raster stack.
3. Performing Principal Component Analysis (PCA) : Identify how well the sampled sites represent the environmental variability of the study area
4. Calculating Environmental Distance to Sampled Sites: Compute the minimum environmental distance between each point in the study area and the closest sampled breeding site.
5. Generating an Environmental Bias Map: Convert the computed environmental distances into a raster layer for visualization and analysis.
Presence Data: A .csv file with at least two columns: longitude, latitude (WGS84 projection recommended).
Environmental Data: Raster layers (.tif format) representing environmental variables (e.g., spectral indices, textural indices, percentage of land cover, temperature, humidity).