A key goal of ecological research is to obtain reliable estimates of population demographic rates, abundance and trends. However, a common challenge when studying wildlife populations is imperfect detection or breeding observation, which results in unknown survival status and reproductive output for some individuals. It is important to account for undetected individuals in population models because they contribute to population abundance and dynamics, and can have implications for population management. Promisingly, recent methodological advances provide us with the tools to integrate data from multiple independent sources to gain insights into the unobserved component of populations. We use data from five reintroduced populations of a threatened New Zealand bird, the hihi (Notiomystis cincta), to develop an integrated population modelling framework that allows missing values for survival status, sex and reproductive output to be modelled. Our approach combines parallel matrices of encounter and reproduction histories from marked individuals, as well as counts of unmarked recruits detected at the start of each breeding season. Integrating these multiple data types enabled us to simultaneously model survival and reproduction of detected individuals, undetected individuals and unknown (never detected) individuals to derive parameter estimates and projections based on all available data, thereby improving our understanding of population dynamics and enabling full propagation of uncertainty. The methods presented will be especially useful for management programmes for populations that are intensively monitored but where individuals are still imperfectly detected, as will be the case for most threatened wild populations.

生态研究的一个关键目标是获得可靠的人口统计数据，数量和趋势估计。然而，在研究野生动植物种群时，一个普遍的挑战是检测或繁殖观察的不完善，这会导致未知的生存状况和某些人的生殖输出。在人口模型中考虑未被发现的个体很重要，因为它们会导致人口数量和动态变化，并可能对人口管理产生影响。可以肯定的是，最近的方法学进步为我们提供了整合来自多个独立来源的数据的工具，以洞悉未观察到的人口组成部分。我们使用来自濒临灭绝的新西兰鸟类hihi（Notiomystis cincta）的五个重新引入种群的数据），以开发一个集成的人口建模框架，该框架可以对生存状态，性别和生殖输出的缺失值进行建模。我们的方法结合了来自标记个体的遭遇和繁殖历史的平行矩阵，以及在每个繁殖季节开始时检测到的未标记新兵的数量。整合这些多种数据类型使我们能够同时对检测到的个体，未检测到的个体和未知（从未检测到）的个体的生存和繁殖进行建模，从而基于所有可用数据得出参数估计和预测，从而增进我们对种群动态的理解并实现对个体的全面传播。不确定。