Frogs are an indicator species. This means they are a go-to for scientists wanting to find out more about the environmental health of a particular ecosystem. Due to their permeable skin, frogs are very sensitive to pollutants, and because they can live on both land and in the water, they are a good indicator of the health of these two different environments. Frogs are poorly served by existing species distribution models. They have very localized distributions, more restricted than suggested by a potentially suitable habitat, and therefore existing models struggle to represent their range accurately.
In this problem you will build a computational model that can predict the count of frogs for a specific location using multiple data sets, and to validate your model on two additional locations. (Ref: EY2022-Frog Challenge)
1. Literature review on the existing works.
2. Develop a combined regression model to predict the density of frog population for region Australia , South Africa and Costa Rica using provided climatic variables.
3. Extend the frog density model with additional datasets like Sentinel-2 Level 2A, JRC Global Surface Water, TerraClimate.
20% Theory, 50% Simulations, 30%Writing
Andreas Kamilaris – firstname.lastname@example.org
Chirag Padubidri – email@example.com