Using machine learning to model nontraditional spatial dependence in occupancy data
Using machine learning to model nontraditional spatial dependence in occupancy data
复制标题
使用机器学习对占用数据中的非传统空间依赖性进行建模
DOI：
10.1002/ecy.3563
复制
发表时间：
2021
期刊：
影响因子：
4.8
通讯作者：
Hefley, Trevor J.
中科院分区：
文献类型：
--
作者：
Mohankumar, Narmadha M.;Hefley, Trevor J.
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性。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的空间自相关性，这可能无法模拟非传统的空间依赖性，如不连续和突变。机器学习方法有可能模拟非传统的空间依赖性，但这些方法不能解释观察者的错误，如假缺席。通过结合贝叶斯层次模型和机器学习方法的灵活性，我们提出了一个通用框架来建模占用数据，该框架考虑了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演示我们的框架。我们的研究结果展示了如何对入住率数据中的传统和非传统空间依赖性进行建模，从而使更广泛的空间入住率模型能够用于提高预测准确性和模型充分性。
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 modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to 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 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.