This repository contains a series of documented notebooks as a first approach to Species Distribution Models implemented in Python.
The data used comes from the following sources:
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WorldClim 2.1: Bioclimatic and elevation variables.
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MapBiomas Colombia: Data obtained through Google Earth Engine.
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eBird: Bird observation data (including effort variables and sightings).
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graficas_preprocesamiento_aves_endemicas_gh.ipynbthis notebook contains a series of graphs generated for the different stages of the environmental and observation data processing flow. -
raster_align_for_sdm.ipynbcode to impute environmental data from rasters aligned to the bird observation locations. -
sdm_co.ipynbcode to train a Logistic Regression model under three scenarios: without imbalance treatment, with imbalance treatment using Random Oversampling, and with imbalance treatment using the SMOTE technique.
If you have any questions or suggestions, feel free to open an issue or contact me via email. If you want to know more from my work visit Juan Sebastian Blandon
Please cite this repo as: Ju 5B77 an Sebastian Blandon. (2024). jsblandon/sdm_py: Species Distribution Models for Colombian Endemic Birds 🇨🇴 🦜 🌎 🛰️ 💻 🗺️ (v1.0.2). Zenodo. https://doi.org/10.5281/zenodo.14509837