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一种用于检测捕获-再捕获数据中捕获异质性的正关联检验。

A Test of Positive Association for Detecting Heterogeneity in Capture for Capture-Recapture Data.

作者信息

Jeyam Anita, McCrea Rachel S, Bregnballe Thomas, Frederiksen Morten, Pradel Roger

机构信息

1National Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NF UK.

2Department of Bioscience, Aarhus University, Rønde, Denmark.

出版信息

J Agric Biol Environ Stat. 2018;23(1):1-19. doi: 10.1007/s13253-017-0315-4. Epub 2017 Dec 11.

Abstract

UNLABELLED

The Cormack-Jolly-Seber (CJS) model assumes that all marked animals have equal recapture probabilities at each sampling occasion, but heterogeneity in capture often occurs and should be taken into account to avoid biases in parameter estimates. Although diagnostic tests are generally used to detect trap-dependence or transience and assess the overall fit of the model, heterogeneity in capture is not routinely tested for. In order to detect and identify this phenomenon in a CJS framework, we propose a test of positive association between previous and future encounters using Goodman-Kruskal's gamma. This test is based solely on the raw capture histories and makes no assumption on model structure. The development of the test is motivated by a dataset of Sandwich terns (), and we use the test to formally show that they exhibit heterogeneity in capture. We use simulation to assess the performance of the test in the detection of heterogeneity in capture, compared to existing and corrected diagnostic goodness-of-fit tests, Leslie's test of equal catchability and Carothers' extension of the Leslie test. The test of positive association is easy to use and produces good results, demonstrating high power to detect heterogeneity in capture. We recommend using this new test prior to model fitting as the outcome will guide the model-building process and help draw more accurate biological conclusions. Supplementary materials accompanying this paper appear online.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary materials for this article are available at 10.1007/s13253-017-0315-4.

摘要

未标记

Cormack-Jolly-Seber(CJS)模型假定所有被标记的动物在每次抽样时具有相等的重捕概率,但捕获过程中的异质性经常出现,应予以考虑以避免参数估计中的偏差。尽管诊断测试通常用于检测陷阱依赖性或瞬态性并评估模型的整体拟合度,但捕获过程中的异质性并未常规进行检验。为了在CJS框架中检测和识别这种现象,我们提出了一种使用古德曼-克鲁斯卡尔γ系数检验先前和未来相遇之间正相关的方法。该检验仅基于原始捕获历史,不对模型结构做任何假设。该检验的提出是受一个三趾鸥数据集的启发,并且我们使用该检验正式表明它们在捕获过程中表现出异质性。与现有的和校正后的诊断拟合优度检验、莱斯利等捕获率检验以及卡罗瑟斯对莱斯利检验的扩展相比,我们使用模拟来评估该检验在检测捕获过程中异质性方面的性能。正相关检验易于使用且产生良好结果,显示出检测捕获过程中异质性的强大能力。我们建议在模型拟合之前使用这种新检验,因为其结果将指导模型构建过程并有助于得出更准确的生物学结论。本文的补充材料在线提供。

电子补充材料

本文的补充材料可在10.1007/s13253-017-0315-4获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42c/6954010/3dc3a6ee50f4/13253_2017_315_Fig1_HTML.jpg

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