In the last several years the use of neural networks as tools to automate species classification from digital data has increased. This has been due in part to the high classification accuracy of image classification through Convolutional Neural Networks (CNN). In the case of audio data CNN based recognizers are used to automate the classification of species in audio recordings by using information from sound visualization (i.e., spectrograms). It is common for these recognizers to use the spectrogram as their sole input. However, researchers have other non-audio data, such as habitat preferences of a species, phenology, and range information, available that could improve species classification. In this paper we present how a single-species recognizer neural network's accuracy can be improved by using non-audio data as inputs in addition to spectrogram information. We also analyze if the improvements are merely a result of having a neural network with a higher number of parameters instead of combining the two inputs. We find that networks that use the two different inputs have a higher classification accuracy than networks of similar size that use only one of the inputs.