Kendall William L, Conn Paul B, Hines James E
USGS Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA.
Ecology. 2006 Jan;87(1):169-77. doi: 10.1890/05-0637.
Matrix population models that allow an animal to occupy more than one state over time are important tools for population and evolutionary ecologists. Definition of state can vary, including location for metapopulation models and breeding state for life history models. For populations whose members can be marked and subsequently reencountered, multistate mark-recapture models are available to estimate the survival and transition probabilities needed to construct population models. Multistate models have proved extremely useful in this context, but they often require a substantial amount of data and restrict estimation of transition probabilities to those areas or states subjected to formal sampling effort. At the same time, for many species, there are considerable tag recovery data provided by the public that could be modeled in order to increase precision and to extend inference to a greater number of areas or states. Here we present a statistical model for combining multistate capture-recapture data (e.g., from a breeding ground study) with multistate tag recovery data (e.g., from wintering grounds). We use this method to analyze data from a study of Canada Geese (Branta canadensis) in the Atlantic Flyway of North America. Our analysis produced marginal improvement in precision, due to relatively few recoveries, but we demonstrate how precision could be further improved with increases in the probability that a retrieved tag is reported.
允许动物随时间占据多个状态的矩阵种群模型是种群和进化生态学家的重要工具。状态的定义可以不同，包括集合种群模型中的位置和生活史模型中的繁殖状态。对于其成员可以被标记并随后重新发现的种群，可以使用多状态标记重捕模型来估计构建种群模型所需的生存和转移概率。在这种情况下，多状态模型已被证明非常有用，但它们通常需要大量数据，并将转移概率的估计限制在经过正式抽样的那些区域或状态。同时，对于许多物种，公众提供了大量可用于建模的标记回收数据，以便提高精度并将推断扩展到更多区域或状态。在这里，我们提出了一种统计模型，用于将多状态捕获 - 重捕数据（例如，来自繁殖地研究）与多状态标记回收数据（例如，来自越冬地）相结合。我们使用这种方法分析了来自北美大西洋迁徙路线上加拿大鹅（Branta canadensis）研究的数据。由于回收数量相对较少，我们的分析在精度上有边际改善，但我们展示了随着检索到的标记被报告的概率增加，精度如何进一步提高。
