StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling
StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling
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DOI：
10.1609/aaai.v35i1.16129
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Eugene Seo;R. Hutchinson;Xiao Fu;Chelsea Li;Tyler A. Hallman;J. Kilbride;W. Robinson
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文献类型：
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作者：
Eugene Seo;R. Hutchinson;Xiao Fu;Chelsea Li;Tyler A. Hallman;J. Kilbride;W. Robinson
本文重点讨论了计算可持续性和统计生态学中的一个核心任务：物种分布模型（SDM）。在SDM中，一个物种在景观上的出现模式是根据一组位置的观测结果，通过环境特征来预测的。起初，SDM可能看起来是一个二进制分类问题，并且人们可能倾向于使用经典工具（例如，逻辑回归、支持向量机、神经网络）来解决这个问题。然而，野生动物调查在物种观察中引入了结构性噪音（特别是计数不足）。如果不考虑这些观测误差，这些观测误差会使SDM系统性地产生偏差。为了解决SDM的独特挑战，本文提出了一个框架，称为StatEcoNet。具体而言，这项工作采用了统计生态学中的图形生成模型作为所提出的计算框架的骨架，并在框架下仔细集成了神经网络。StatEcoNet的优势，相关方法的模拟数据集，以及鸟类物种的数据证明。由于空间数据模型是生态科学和自然资源管理的关键工具，StatEcoNet可以为具有重大社会影响的广泛应用提供更强的计算和分析能力，例如，研究和保护濒危物种。
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.