  Conservation science depends on an accurate understanding of what's happening
in a given ecosystem. How many species live there? What is the makeup of the
population? How is that changing over time? Species Distribution Modeling (SDM)
seeks to predict the spatial (and sometimes temporal) patterns of species
occurrence, i.e. where a species is likely to be found. The last few years have
seen a surge of interest in applying powerful machine learning tools to
challenging problems in ecology. Despite its considerable importance, SDM has
received relatively little attention from the computer science community. Our
goal in this work is to provide computer scientists with the necessary
background to read the SDM literature and develop ecologically useful ML-based
SDM algorithms. In particular, we introduce key SDM concepts and terminology,
review standard models, discuss data availability, and highlight technical
challenges and pitfalls.
