Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations

Allan Buras, Marieke van der Maaten-Theunissen, Ernst van der Maaten, Svenja Ahlgrimm, Philipp Hermann, Sonia Simard, Ingo Heinrich, Gerd Helle, Martin Unterseher, Martin Schnittler, Pascal Eusemann and Martin Wilmking

PLOS ONE, 2016, vol. 11, issue 7, 1-21

Abstract: 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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0158346

DOI: 10.1371/journal.pone.0158346

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