This project is level 1 of the EY 2022 Better Working World Data Challenge, with the goal of building a local frog discovery tool. The challenge is to predict the occurrence of the frog species Litoria fallax, also known as the Eastern Dwarf Tree Frog. This frog is most commonly found on the eastern coast of Australia, in Cairns, Queensland, Ulladula, and New South Wales. The frog's habitat includes coastal swamps, lagoons, dams, ditches, ponds, forests, heathland, and farmland. This challenge focuses on frogs, because of their importance to the environment and conservation efforts. Frogs are an indicator species that can inform scientists about the overall health of an ecosystem. While this particular species of frog is numerous and is not endangered, many frog species are. This predictive model can ideally be applied to other species of frogs that require conservation and protection.
The frog locations are found using a coarse spatial resolution. Space tech and AI are implimented to monitor biodiversity at scale. The predictor variables used for this challenge are from the TerraClimate dataset available from the Microsoft Planetary Computer portal, where climate data is collected monthly.
The output for this challenge is a Species Distribution Model (SDM) for the Litoria fallax frogs in one specific focus area. SDMs are widely used ecological tools to determine the quantity and distribution of a species, which enable decisions for environmental regulation and conservation.
Jupyter Notebook of my work can be viewed here.