Biodiversity data is becoming increasingly available and its volume is growing rapidly. Integration of different related repositories is also advancing through a number of successful initiatives. This data can be given as input to sophisticated computer models for predicting potential distribution of species. As the amounts of manipulated data increase, the execution of these models require more powerful computational resources and the use of parallel and distributed computing techniques to become scalable. At the same time, it is increasingly difficult to manage simulations given by various computational tasks that explore, for instance, different modeling algorithms or different parameter configurations. In this work we explore applications of scalable and provenance-enabled scientific workflow techniques to ecological niche modeling.