Building accurate knowledge of the identity, the geographic distribution and the evolution of organisms is essential for biodiversity conservation. Automatic prediction of list of species is useful for many scenarios in biodiversity informatics. In this work, we propose a hybrid model to predict the species that are most probable to be observed at a given location, using environmental features and taxonomy of the organism. These environmental features are represented as k-dimensional image patches, where each dimension represents the value of an environmental variable, in the neighborhood of the occurrence of the species. The hybrid model Convolutional Long Short-Term Memory Neural Networks henceforth called as CLNN, is a combination of Convolutional Neural Networks(CNNs) and Long Short-Term Memory Networks(LSTMs), where the CNN forms the spatial feature generator while the LSTM focuses on finding the taxonomy. Using the dataset provided by Geo LifeCLEF 2018, the proposed method helped achieve a Mean Reciprocal Rank (MRR) score of 0.003 during the test phase.