FOSS4G-Asia 2024

Species Distribution Modeling (SDM) is a statistical methodology used to predict the spatial and temporal distribution of species based on environmental conditions that are conducive to their survival and reproduction. This modeling approach leverages spatially explicit species occurrence records alongside various environmental covariates, including climate, terrain, and land cover, as input variables, with the aim of quantifying and mapping species-environment interactions. SDM has become a critical tool in ecological research and conservation biology for understanding and predicting species distribution patterns. A range of machine learning and deep learning techniques can be employed in SDM, such as Logistic Regression (LR), Random Forest (RF), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Generative Adversarial Network-CNN (GAN-CNN). Despite the availability of these techniques, there is a lack of a comprehensive application that integrates these algorithms for species distribution modeling. To address this gap, this paper introduces a new tool, kari-sdm, which enables users to perform SDM utilizing a variety of techniques. Kari-sdm supports LR, RF, MLP, CNN, and GAN-CNN algorithms, all based on open-source frameworks PyTorch and scikit-learn. Additionally, it facilitates all necessary preprocessing steps, from data collection, cleaning, transformation, spatial preprocessing, and environmental variable selection, to data splitting. The tool also provides functions for model evaluation, result visualization, and cross-validation. The primary goal of kari-sdm is to assist ecologists in modeling species distributions, interpreting results, and developing informed conservation and management strategies.