Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation
Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation
复制标题
使用多重插补将遥测误差纳入动物运动的隐马尔可夫模型
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发表时间：
2017
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通讯作者：
B. McClintock
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作者：
B. McClintock
当数据流被无错误地以规则的时间间隔观察时，离散时间隐马尔可夫模型（HHHRM）在分析动物位置和辅助生物遥测数据方面变得非常流行。然而，测量误差和时间不规则的数据往往是普遍的遥测研究，特别是在海洋系统。虽然相对少量的完全随机缺失的缺失数据通常在HALTORY中没有问题，但时间不规则性可能导致很少（如果有的话）观察与HALTORY所需的规则时间步长对齐。明确说明归因于位置测量误差、时间上不规则的观测或其他形式的缺失数据的不确定性的拟合障碍通常需要计算要求高的技术，诸如马尔可夫链蒙特卡罗（MCMC）。使用模拟和现实世界的胡子海豹（Erignathus rectuatus）的例子，我调查了一个实际的替代方案，将测量误差和时间不规则的观测到基于多个插补的单状态连续时间运动模型的位置过程中绘制的障碍。这种两阶段的方法相对简单，使用有效的最大似然方法与现有的软件进行，并完全并行化。我通常发现该方法在广泛的模拟测量误差和不规则采样率范围内表现良好，几乎在所有模拟场景中都能可靠地恢复潜在状态和位置。然而，高的测量误差加上低的采样率往往会引起偏见的估计概率分布的数据流来自插补的位置过程和估计的影响空间协变量的状态转移概率。对须海豹数据进行两阶段分析的结果与计算密集度更高的单阶段MCMC分析相似，但两阶段分析需要的计算时间要少得多，而且无需自定义模型拟合算法。因此，我发现两阶段多重插补方法在易于实现、计算时间和性能方面很有前途。本文提供了使用R包“momentuHMM”实现该方法的代码。
When data streams are observed without error and at regular time intervals, discrete-time hidden Markov models (HMMs) have become immensely popular for the analysis of animal location and auxiliary biotelemetry data. However, measurement error and temporally irregular data are often pervasive in telemetry studies, particularly in marine systems. While relatively small amounts of missing data that are missing-completely-at-random are not typically problematic in HMMs, temporal irregularity can result in few (if any) observations aligning with the regular time steps required by HMMs. Fitting HMMs that explicitly account for uncertainty attributable to location measurement error, temporally irregular observations, or other forms of missing data typically requires computationally demanding techniques, such as Markov chain Monte Carlo (MCMC). Using simulation and a real-world bearded seal (Erignathus barbatus) example, I investigate a practical alternative to incorporating measurement error and temporally irregular observations into HMMs based on multiple imputation of the position process drawn from a single-state continuous-time movement model. This two-stage approach is relatively simple, performed with existing software using efficient maximum likelihood methods, and completely parallelizable. I generally found the approach to perform well across a broad range of simulated measurement error and irregular sampling rates, with latent states and locations reliably recovered in nearly all simulated scenarios. However, high measurement error coupled with low sampling rates often induced bias in both the estimated probability distributions of data streams derived from the imputed position process and the estimated effects of spatial covariates on state transition probabilities. Results from the two-stage analysis of the bearded seal data were similar to a more computationally intensive single-stage MCMC analysis, but the two-stage analysis required much less computation time and no custom model-fitting algorithms. I thus found the two-stage multiple-imputation approach to be promising in terms of its ease of implementation, computation time, and performance. Code for implementing the approach using the R package “momentuHMM” is provided.Supplementary materials accompanying this paper appear online.