1. Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade‐offs between capturing ecological complexity in statistical models and implementing these models.
2. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation‐assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual‐ and population‐level migratory characteristics.
3. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm.
4. For complex ecological statistical models, like those for animal movement, multi‐species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation‐assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.

中文翻译：

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生态学中层次模型的层次计算

1. 贝叶斯层次模型允许生态学家考虑不确定性并在多个尺度上进行推断。但是，层次模型通常需要大量的计算才能适应，特别是对于大型数据集，研究人员面临着在统计模型中捕获生态复杂性与实现这些模型之间进行权衡的问题。
2. 我们提出了一种递归贝叶斯计算（RB）方法，可用于在连续MCMC阶段高效地拟合贝叶斯模型，以简化计算并简化层次推理。我们还引入了变换辅助RB（TARB），以创建无监督的MCMC算法并提高参数的可解释性。我们通过拟合分层动物运动模型来论证TARB，以获得关于个人和人口水平的迁徙特征的推断。
3. 与使用单个MCMC算法拟合模型相比，我们的递归程序将用于拟合层次运动模型的计算时间减少了一半。我们获得了使用TARB拟合模型的相同推理，这与使用单个算法拟合模型的推理相同。
4. 对于复杂的生态统计模型，如动物运动，多物种系统或较大的时空尺度，采用传统计算技术拟合模型的计算需求可能会限制模型规格，从而阻碍科学发现。转型辅助的RB是减少这些限制的最便捷的方法之一，使我们能够实施新的统计模型并加深对复杂生态现象的理解。