生态学中的物候模式:使用循环统计的问题和基于模拟的解决方案

IF 6.3 2区 环境科学与生态学 Q1 ECOLOGY Methods in Ecology and Evolution Pub Date : 2024-04-05 DOI:10.1111/2041-210X.14316
Michael R. Willig, Julissa Rojas-Sandoval, Steven J. Presley
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引用次数: 0

摘要

物候模式(如迁徙、冬眠或繁殖)的量化应包括对非均匀时间模式的统计评估。循环统计(如瑞利检验或赫尔曼斯-拉森检验)根据在若干时间间隔内表现出特定活动的个体数量提供了有用的方法。本研究以月度繁殖活动为例,说明了在边际总数是实验设计特征的情况下(例如,每个时间间隔的繁殖活跃个体数量取决于取样努力或取样成功率),将循环统计应用于数据时所遇到的问题。我们通过制作四个示例数据集和开发自举模拟程序来说明这一问题的本质,从而克服因边际总数的存在而产生的复杂性。此外,我们还将循环统计和引导模拟应用于亚马逊地区六种新热带蝙蝠的繁殖物候学经验数据。由于不同时间间隔的取样努力或成功率可能不同,循环统计可能会产生两种误导性结果:当经验模式具有明显的模式性时,循环统计表明物候学是均匀的;当经验模式是均匀的时,循环统计表明物候学是不均匀的。自举模拟克服了这些局限性:根据自举方法,生殖活跃个体百分比为模态的示例物候被恰当地确定为非均匀物候,而生殖活跃个体百分比为不变的示例物候则被恰当地确定为均匀物候。根据自举法,六个经验范例的生殖物候都是不均匀的,对于具有单峰或双峰的蝙蝠物种来说都是如此。除了边际总数的问题,对生态学中物候模式分析的回顾还发现了循环统计应用中的另外两个常见问题:抽样偏差和伪重复。本文还讨论了这两个问题以及潜在的解决方案。通过提供执行瑞利检验和赫尔曼斯-拉森检验的源代码以及引导模拟的代码,我们提供了一种有用的工具,用于在边际总量成为实验设计特征时评估非随机物候。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Phenological patterns in ecology: Problems using circular statistics and solutions based on simulations

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来源期刊
CiteScore
11.60
自引率
3.00%
发文量
236
审稿时长
4-8 weeks
期刊介绍: A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.
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