有效利用人口统计数据:综合人口模型

Marlène Gamelon, S. Vriend, M. Visser, C. Hallmann, S. Lommen, E. Jongejans
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引用次数: 1

摘要

可以在现场收集各种类型的人口统计数据:人口普查、捕获-标记-再捕获数据等等。这些数据来源共享有关所研究人口的共同人口统计信息。贝叶斯综合人口模型(IPM)通过对这些不同类型的人口数据进行联合分析,有效地利用了这些数据。本章讨论了这种综合方法的优点和可能性。它描述了构建IPM所需的不同步骤,并使用两个案例研究说明了这种方法的有用性。第一个案例研究是一种短寿鸟类,蓝山雀,利用在荷兰种群中收集的不同数据来源来强调综合分析如何有助于获得其动态的全面图景。该IPM还评估了山毛榉作物的大小是否以及如何影响生命率。第二个案例研究是一种入侵植物,常见的豚草。本章说明了如何同时分析幼苗数据、植物数据和种子库数据,以估计关键的生命率,例如幼苗存活到开花的概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient use of demographic data: integrated population models
Various types of demographic data can be collected in the field: population censuses, capture–mark–recapture data, and so on. These data sources share common demographic information about the studied population. Bayesian integrated population models (IPM) make efficient use of these different types of demographic data by jointly analysing them. This chapter discusses the advantages and the possibilities offered by this integrated approach. It describes the different steps required to build an IPM and illustrates the usefulness of this approach using two case studies. The first case study is a short-lived bird species, the blue tit, taking advantage of different data sources collected in a Dutch population to highlight how an integrated analysis might help to obtain a comprehensive picture of its dynamics. This IPM also assesses whether and how beech crop size might influence vital rates. The second case study is an invasive plant species, the common ragweed. The chapter illustrates how seedling data, plant data, and seed bank data could be analysed simultaneously to estimate key vital rates such as the probability that a seedling survives up to flowering.
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