Tracking individual animals through time using mark-recapture methods is the gold standard for understanding how environmental conditions influence demographic rates, but applying such tags is often infeasible due to the difficulty of catching animals or attaching marks/tags without influencing behavior or survival. Due to the logistical challenges and emerging ethical concerns with flipper banding penguins, relatively little is known about spatial variation in demographic rates, spatial variation in demographic stochasticity, or the role that stochasticity may play in penguin population dynamics. Here we describe how adaptive importance sampling can be used to fit age-structured population models to time series of point counts. While some demographic parameters are difficult to learn through point counts alone, others can be estimated, even in the face of missing data. Here we demonstrate the application of adaptive importance sampling using two case studies, one in which we permit immigration and another permitting regime switching in reproductive success. We apply these methods to extract demographic information from several time series of observed abundance in gentoo and Adélie penguins in Antarctica. Our method is broadly applicable to time series of abundance and provides a feasible means of fitting age-structured models without marking individuals.

使用标记重新捕获方法通过时间跟踪个体动物是了解环境条件如何影响人口统计率的黄金标准，但由于难以捕捉动物或在不影响行为或生存的情况下附加标记/标签，因此应用此类标签通常是不可行的。由于鳍状带企鹅的后勤挑战和新出现的伦理问题，人们对人口统计率的空间变化、人口随机性的空间变化或随机性在企鹅种群动态中可能发挥的作用知之甚少。在这里，我们描述了如何使用自适应重要性抽样将年龄结构的人口模型拟合到点计数的时间序列。虽然仅通过点数很难学习一些人口统计参数，但可以估计其他一些参数，即使面对丢失的数据。在这里，我们使用两个案例研究展示了自适应重要性抽样的应用，其中一个我们允许移民，另一个允许在繁殖成功中进行制度转换。我们应用这些方法从南极洲巴布亚企鹅和阿德利企鹅观察到的几个时间序列中提取人口统计信息。我们的方法广泛适用于丰度时间序列，并提供了一种在不标记个体的情况下拟合年龄结构模型的可行方法。我们应用这些方法从南极洲巴布亚企鹅和阿德利企鹅观察到的几个时间序列中提取人口统计信息。我们的方法广泛适用于丰度时间序列，并提供了一种在不标记个体的情况下拟合年龄结构模型的可行方法。我们应用这些方法从南极洲巴布亚企鹅和阿德利企鹅观察到的几个时间序列中提取人口统计信息。我们的方法广泛适用于丰度时间序列，并提供了一种在不标记个体的情况下拟合年龄结构模型的可行方法。