Deep Multi-species Embedding

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 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.

Authors

• Di Chen Nanjing University of Science and Technology
• Yexiang Xue Department of Computer Science, Purdue University
• Daniel Fink
• Shuo Chen
• Carla P. Gomes Department of Computer Science, Cornell University, USA

Keywords

• Machine Learning: Deep Learning
• Machine Learning: Structured Learning
• Multidisciplinary Topics and Applications: AI and Natural Sciences
• Multidisciplinary Topics and Applications: Computational Sustainability