  Understanding how species are distributed across landscapes over time is a
fundamental question in biodiversity research. Unfortunately, most species
distribution models only target a single species at a time, despite strong
ecological evidence that species are not independently distributed. We propose
Deep Multi-Species Embedding (DMSE), which jointly embeds vectors corresponding
to multiple species as well as vectors representing environmental covariates
into a common high-dimensional feature space via a deep neural network. Applied
to bird observational data from the citizen science project \textit{eBird}, we
demonstrate how the DMSE model discovers inter-species relationships to
outperform single-species distribution models (random forests and SVMs) as well
as competing multi-label models. Additionally, we demonstrate the benefit of
using a deep neural network to extract features within the embedding and show
how they improve the predictive performance of species distribution modelling.
An important domain contribution of the DMSE model is the ability to discover
and describe species interactions while simultaneously learning the shared
habitat preferences among species. As an additional contribution, we provide a
graphical embedding of hundreds of bird species in the Northeast US.
