Bayesian methods are increasingly being applied to parameterize mechanistic process models used in environmental prediction and forecasting. In particular, models describing ecosystem dynamics with multiple states that are linear and autoregressive at each step in time can be treated as statistical state space models. In this paper we examine this subset of ecosystem models, giving closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. We use simulated data from an example model (DALECev) to assess the performance of parameter estimation and identifiability under scenarios of gaps in observations. We show that process precision estimates become unreliable as temporal gaps between observed state data increase. To improve estimates, particularly precisions, we introduce a method of tuning the timestep of the latent states to leverage higher-frequency driver information. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where 1) data are not available for all states and transfers at the operational timestep for the ecosystem model and 2) process uncertainty estimation is desired.
翻译:越来越多的贝叶斯方法被应用于环境预测和预测中使用的机械化过程模型的参数化。特别是,描述生态系统动态的模型,在每一阶段都具有线性和自动递减性,这些模型可以被视为统计国家空间模型。在本文件中,我们审查了这一组生态系统模型,为潜在状态提供了封闭形式的Gibs抽样更新,并在通常分布过程和观察错误时提供了过程精确参数。我们使用一个样板模型(DALECev)的模拟数据来评估参数估计的性能和在观测差距的假想情况下的可识别性。我们表明,随着观察到的状态数据之间的时间差距增加,过程精确估计变得不可靠。为了改进估计,特别是精确度,我们采用了调整潜在国家的时间步数的方法,以利用高频率驱动器信息。此外,我们表明数据克隆是评估该类模型参数可识别性的适当方法。总体而言,我们的研究有助于将国家空间模型应用于生态预报应用,其中(1)没有为所有州提供数据,在生态系统模型的操作时间步骤和(2)过程不确定性估计需要转移。