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.
翻译:物种分布模型(SDM)试图预测物种发生的空间(有时是时间)模式,即可能发现物种的地点。在过去几年里,人们对于应用强大的机器学习工具来挑战生态问题的兴趣激增。SDM尽管具有相当大的重要性,但从计算机科学界得到的关注相对较少。我们的工作目标是向计算机科学家提供必要的背景来阅读SDM文献和开发基于生态的ML SDM算法。我们特别引入了关键的SDM概念和术语,审查标准模型,讨论数据的可得性,并突出技术挑战和陷阱。