This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to be a binary classification problem, and one might be inclined to employ classic tools (e.g., logistic regression, support vector machines, neural networks) to tackle it. However, wildlife surveys introduce structured noise (especially under-counting) in the species observations. If unaccounted for, these observation errors systematically bias SDMs. To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet. Specifically, this work employs a graphical generative model in statistical ecology to serve as the skeleton of the proposed computational framework and carefully integrates neural networks under the framework. The advantages of StatEcoNet over related approaches are demonstrated on simulated datasets as well as bird species data. Since SDMs are critical tools for ecological science and natural resource management, StatEcoNet may offer boosted computational and analytical powers to a wide range of applications that have significant social impacts, e.g., the study and conservation of threatened species.

本文提出了一个名为StatEcoNet的框架，将图形生成模型应用于统计生态学，并在该框架下巧妙地融合神经网络，以应对物种分布建模中的挑战。使用模拟数据集和鸟类数据展示了StatEcoNet相对于相关方法的优越性，可以对生态科学和自然资源管理等领域的研究和保护威胁物种提供提升的计算和分析能力。

StatEcoNet: 物种分布建模的统计生态神经网络