Species connectivity models play an important role in ecological research and biodiversity assessment. Unfortunately, simulations of connectivity models are typically slow, therefore preventing the rapid iteration and updates of models when evaluating different scenarios. In this pilot study, we present the proof of concept of utilizing Deep Learning methodologies as a novel approach in ecology for significantly reducing the predic- tion rate of species connectivity models.