# Species Distribution Modeling applied to _Aedes albopictus_ breeding sites in public spaces (Montpellier) and generating an environmental bias map
## Description
## Getting started
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.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
## Installation
Before running the scripts, make sure you have the following R packages installed:
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
**1. Species Distribution Modeling (SDM)**
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
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:
-[ ] [Set up project integrations](https://forge.ird.fr/espace-dev/personnels/teillet/biomod2-and-sampling-biais/-/settings/integrations)
1. Loading Data: Import presence data and raster layers of environmental variables.
## Collaborate with your team
2. Data Formatting: Prepare the data for biomod2.
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
3. Running Individual Models: Train different SDM algorithms and evaluate their performance.
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
7. Projection: Apply the final model to the entire study area to generate probability maps of breeding site presence.
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
**2. Sampling Bias Analysis**
## Suggestions for a good README
Script: sampling_bias_mtp.R
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
This script analyzes potential sampling biases by comparing the spatial distribution of presence data with the underlying environmental variables. It includes:
## Name
1. Loading data
Choose a self-explaining name for your project.
## Description
2. Extraction Environmental Characteristics : Extract environmental variables at breeding site locations from the environmental raster stack.
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
3. Performing Principal Component Analysis (PCA) : Identify how well the sampled sites represent the environmental variability of the study area
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals
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.
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Installation
5. Generating an Environmental Bias Map: Convert the computed environmental distances into a raster layer for visualization and analysis.
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
## Support
##Data Requirements
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
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).
## Roadmap
##Output
If you have ideas for releases in the future, it is a good idea to list them in the README.
## Contributing
- Model performance metrics (AUC, TSS)
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
- Variable importance scores
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
- Response curves
## Authors and acknowledgment
- Probability maps of breeding site presence
Show your appreciation to those who have contributed to the project.
## License
- Uncertainty associated with the probabilities
For open source projects, say how it is licensed.
## Project status
- Environmental Bias Map
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
