论文标题
具有时间优先取样以推断物候学的动态人群模型
Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology
论文作者
论文摘要
为了研究人群动态，生态学家和野生动植物生物学家使用相对丰度数据，这些数据通常受到时间优先采样。当抽样工作在随时间变化时，就会发生时间优先采样。为了考虑优先采样，我们指定了一个贝叶斯分层丰度模型，该模型考虑了观察时间和感兴趣的生态过程之间的依赖性。提出的模型改善了在不经常观察期间的丰度估计，并考虑了离散时间内时间优先采样的时间。此外，我们的模型促进了人口增长率和机械现象学的后验推断。我们应用模型来分析国家生态观测网络收集的模拟数据和蚊子计数数据。在第二个案例研究中，我们表征了艾德斯属中几种蚊子物种的种群增长率和丰度。
To study population dynamics, ecologists and wildlife biologists use relative abundance data, which are often subject to temporal preferential sampling. Temporal preferential sampling occurs when sampling effort varies across time. To account for preferential sampling, we specify a Bayesian hierarchical abundance model that considers the dependence between observation times and the ecological process of interest. The proposed model improves abundance estimates during periods of infrequent observation and accounts for temporal preferential sampling in discrete time. Additionally, our model facilitates posterior inference for population growth rates and mechanistic phenometrics. We apply our model to analyze both simulated data and mosquito count data collected by the National Ecological Observatory Network. In the second case study, we characterize the population growth rate and abundance of several mosquito species in the Aedes genus.
