There are 1 repository under species-distribution-modeling topic.
Faster, better, smarter ecological niche modeling and species distribution modeling
Spatial Implicit Neural Representations for Global-Scale Species Mapping - ICML 2023
Tools for Modeling Niches and Distributions of Species
A Python Package for Adaptive Spatio-Temporal Exploratory Model (AdaSTEM)
Introductory lesson to generate range maps for butterfly-host plant interactions and predict distributional shifts using publicly available biodiversity data and data science tools
Slides for the "Interpretable SDM with Julia" workshop
"Trade-of between deep learning for species identication...", by Olivier Gimenez et al
Modeling vulnerability of ruffed lemurs to climate change and deforestation
Locust breeding ground prediction using pseudo-absence generation and machine learning.
A Python class for AutoML spatial classification (designed for Species Distribution Modeling applications).
Utilize R and GBIF to automatically download data and run optimized MaxEnt models for multiple species.
Environmental Interpolation using Spatial Kernel Density Estimation
Submission to the GeoLifeCLEF 2019 Species Recommendation Challenge
R package for vizzuality species distrubtion prediction tools
Code for modeling climate variables in the Canary Islands
Proof-of-concept species distribution modeling package intended for learning and experimentation
Application of Taylor Diagrams to Ecological Niche Models/Species Distribution Models
Workflow to fit niche models to species occurrence data using glmnet via maxnet package
Predicting the Future Distribution of Leucobryum aduncum under Climate Change
Material for 1 week course in species distribution modeling using R notebooks, MaxEnt, and GIS packages.
Species Distribution Model for Rodriguez et al. (2022): Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago.
A Graphical User Interface-Based R Package for Species Distribution Modeling
This is a file to predict 9 species of frogs based on the coordinates provided by Ernst & Young for the 2022 Data Challenge. This scored .77 against the actual data stored in the EY challenge hub for submission grading.
Connecting to External Data Sources, APIs, and Simple Distribution Map Exercise for BIOL 5700, Advanced Data Analytics
R package to give easy access to ALA plant occurrence data