Integrated Population Models: Achieving Their Potential
Integrated Population Models: Achieving Their Potential
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DOI：
10.1007/s42519-022-00302-7
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发表时间：
2022-11
影响因子：
0.6
通讯作者：
Fay Frost;R. McCrea;Ruth King;O. Gimenez;Elise F. Zipkin
中科院分区：
文献类型：
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作者：
Fay Frost;R. McCrea;Ruth King;O. Gimenez;Elise F. Zipkin
精确和准确的丰度和人口统计率的估计是在野生动物保护和管理的主要利益数量。这些数量提供了对人口随时间变化的趋势以及系统相关的潜在生态驱动因素的洞察。这些信息对于管理生态系统、评估物种保护状况以及制定和实施有效的保护政策至关重要。观察性监测数据通常是根据主要关注的问题，使用一系列不同的调查协议收集的。对于每一种调查设计，都开发了一系列先进的统计技术，这些技术通常都很容易理解。然而，对于研究中的同一人群，往往可能存在多种类型的数据。单独分析每个数据集隐含地丢弃了包含在其他数据集中的公共信息。另一种旨在优化包含在多个数据集中的共享信息的方法是使用“基于模型的数据集成”方法，或者更通常地称为“集成模型”。这种集成的建模方法可以在一个强大的统计框架内同时分析所有可用的数据。本文提供了一个生态综合模型的统计概述，重点是综合人口模型（IPM），其中包括丰富和人口率作为感兴趣的数量。在这一领域的四个主要挑战进行了讨论，即模型规格，计算方面，模型评估和预测。这将鼓励研究人员进一步探索和开发新的实用工具，以确保IPM在未来的研究中得到充分利用。
Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.