使用机器学习识别占用数据中的非传统空间依赖
Using machine learning to identify nontraditional spatial dependence in occupancy data
摘要：
占用数据的空间模型用于估计和绘制物种的真实存在，这可能取决于生物和非生物因素以及空间自相关。传统上，研究人员通过使用相关的正态分布的站点水平随机效应来解释占用数据中的空间自相关，这可能无法识别非传统的空间依赖性，例如不连续性和突然过渡。机器学习方法具有识别和建模非传统空间依赖性的潜力，但是这些方法不考虑观察者错误，例如假缺席。通过结合贝叶斯分层建模和机器学习方法的灵活性，我们提出了一个通用的框架来模拟占用数据，占传统和非传统的空间依赖以及假缺席。我们使用六个合成占用数据集和两个真实数据集来展示我们的框架。我们的研究结果展示了如何识别和建模占用数据中的传统和非传统空间依赖性，这使得更广泛的一类空间占用模型，可用于提高预测精度和模型充分性。
英文摘要：
Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of identifying nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to identify and model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to identify and model both traditional and nontraditional spatial dependence in occupancy data which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.
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