Extending the CWM approach to intraspecific trait variation: how to deal with overly optimistic standard tests?

IF 2.3 2区 环境科学与生态学 Q2 ECOLOGY Oecologia Pub Date : 2024-06-01 Epub Date: 2024-05-28 DOI:10.1007/s00442-024-05568-1
David Zelený, Kenny Helsen, Yi-Nuo Lee
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Abstract

Community weighted means (CWMs) are widely used to study the relationship between community-level functional traits and environment. For certain null hypotheses, CWM-environment relationships assessed by linear regression or ANOVA and tested by standard parametric tests are prone to inflated Type I error rates. Previous research has found that this problem can be solved by permutation tests (i.e., the max test). A recent extension of the CWM approach allows the inclusion of intraspecific trait variation (ITV) by the separate calculation of fixed, site-specific, and intraspecific CWMs. The question is whether the same Type I error rate inflation exists for the relationship between environment and site-specific or intraspecific CWM. Using simulated and real-world community datasets, we show that site-specific CWM-environment relationships have also inflated Type I error rate, and this rate is negatively related to the relative ITV magnitude. In contrast, for intraspecific CWM-environment relationships, standard parametric tests have the correct Type I error rate, although somewhat reduced statistical power. We introduce an ITV-extended version of the max test, which can solve the inflation problem for site-specific CWM-environment relationships and, without considering ITV, becomes equivalent to the "original" max test used for the CWM approach. We show that this new ITV-extended max test works well across the full possible magnitude of ITV on both simulated and real-world data. Most real datasets probably do not have intraspecific trait variation large enough to alleviate the problem of inflated Type I error rate, and published studies possibly report overly optimistic significance results.

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将 CWM 方法扩展到种内性状变异:如何处理过于乐观的标准测试?
群落加权平均值(CWMs)被广泛用于研究群落级功能性状与环境之间的关系。对于某些零假设,通过线性回归或方差分析评估并通过标准参数检验检验的 CWM 与环境的关系容易出现 I 类错误率过高的问题。以前的研究发现,这个问题可以通过置换检验(即最大值检验)来解决。最近对 CWM 方法进行了扩展,允许通过单独计算固定的、特定地点的和特定地点内的 CWM 来纳入种内性状变异(ITV)。问题是,环境与特定地点或特定种内 CWM 之间的关系是否存在相同的 I 类错误率膨胀。利用模拟和真实世界的群落数据集,我们发现特定地点的 CWM 与环境的关系也存在 I 类错误率膨胀,且该错误率与相对 ITV 的大小呈负相关。相反,对于种内 CWM-环境关系,标准参数检验的 I 类错误率是正确的,但统计能力有所下降。我们引入了最大值检验的 ITV 扩展版本,它可以解决特定地点 CWM 环境关系的膨胀问题,并且在不考虑 ITV 的情况下,等同于用于 CWM 方法的 "原始 "最大值检验。我们的研究表明,这种新的 ITV 扩展最大值检验在模拟数据和真实世界数据的全部可能 ITV 幅值范围内都运行良好。大多数真实数据集的种内性状变异可能没有大到足以缓解 I 类错误率膨胀问题的程度,已发表的研究可能会报告过于乐观的显著性结果。
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来源期刊
Oecologia
Oecologia 环境科学-生态学
CiteScore
5.10
自引率
0.00%
发文量
192
审稿时长
5.3 months
期刊介绍: Oecologia publishes innovative ecological research of international interest. We seek reviews, advances in methodology, and original contributions, emphasizing the following areas: Population ecology, Plant-microbe-animal interactions, Ecosystem ecology, Community ecology, Global change ecology, Conservation ecology, Behavioral ecology and Physiological Ecology. In general, studies that are purely descriptive, mathematical, documentary, and/or natural history will not be considered.
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