Assessing Ecosystem State Space Models: Identifiability and Estimation
Assessing Ecosystem State Space Models: Identifiability and Estimation
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DOI：
10.1007/s13253-023-00531-8
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发表时间：
2023-03-09
影响因子：
1.4
通讯作者：
Thomas，R. Q.
中科院分区：
文献类型：
--
作者：
Smith Jr，J. W.;Johnson，L. R.;Thomas，R. Q.
在环境预测和预报中，分层概率模型比非分层确定性过程模型更频繁地被使用，贝叶斯方法拟合这类模型正变得越来越流行。具体地说，描述生态系统动力学的模型可以被视为统计状态空间模型(SSM)，这些模型在每个时间步具有自回归的多状态。本文研究了这类生态系统模型的子集，将一个基于过程的生态系统模型嵌入到SSM中，并给出了过程误差和观测误差为正态分布时潜在状态和过程精度参数的闭合形式的Gibbs抽样更新。在这里，我们使用一个示例模式(DALECev)的模拟数据，研究改变观测的时间分辨率对状态(观测数据间隙)、状态过程的时间分辨率(模型时间步长)以及观测对通量的聚集程度(对状态过程的转移率的测量)的影响。我们表明，随着观测状态数据之间的时间间隔的增加，参数估计变得不可靠。为了改进参数估计，我们引入了一种调整潜态的时间分辨率的方法，同时仍然使用更高频率的驱动信息，并表明这有助于改善估计。进一步，我们证明了数据克隆是评估这类模型中参数可辨识性的一种合适方法。总体而言，我们的研究有助于将状态空间模型应用于生态预测应用中，其中(1)不是所有状态的数据都可用，并且在生态系统模型的运行时间步长传输数据是可用的，(2)需要进行过程不确定性估计。
Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. 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 time step for the ecosystem model and (2) process uncertainty estimation is desired.