Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.
Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.
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DOI：
10.1371/journal.pone.0158346
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Wilmking M
中科院分区：
文献类型：
--
作者：
Buras A;van der Maaten-Theunissen M;van der Maaten E;Ahlgrimm S;Hermann P;Simard S;Heinrich I;Helle G;Unterseher M;Schnittler M;Eusemann P;Wilmking M
本文介绍了一种新的方法-主成分梯度分析（PCGA）-检测生态梯度的时间序列种群，即几个时间序列起源于不同的个人的人口。生态梯度的检测是特别重要的，当处理时间序列从异质种群表达不同的趋势。PCGA利用通过主成分分析（PCA）获得的前两个轴的载荷的极坐标来定义相似趋势的组。基于平均序列间相关性（rbar），使用蒙特卡罗模拟来量化通过PCGA组增加共同基础信号的增益。在验证PCGA相比，其他三个现有的方法。专注于dendrochronous的例子，PCGA正确地确定人口梯度，并在特定情况下，是优于其他考虑的方法。此外，每个示例中的PCGA组允许增强共同的潜在信号的强度，并且允许分层聚类分析。我们的研究结果表明，PCGA可能允许更好地了解机制，造成时间序列的人口梯度，以及客观地提高在树木年轮气候学的气候转换函数的性能。虽然我们的例子突出了PCGA的树木年代学领域的相关性，我们相信，也与可比结构的数据工作的其他学科可能会受益于PCGA。
This paper introduces a new approach–the Principal Component Gradient Analysis (PCGA)–to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.