Climate Change Increases Drought Stress of Juniper Trees in the Mountains of Central Asia - Fig 4

<p><b>Changes in juniper growth from the 1935–1964 to 1982–2011 period a) spatially interpolated from the 33 sites (black dots) and b) shown as a function of altitude.</b> Red dots denote the sites closest CRU grid point.</p

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

<div><p>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.</p></div

Changes in climate response for the June–August (JJA) season of all 33 sites (black dots) with its closest CRU grid point dataset (red dots) from the 1935–1964 to 1982–2011 period.

<p>Differences in <b>a)</b> temperature and <b>c)</b> precipitation response were spatially interpolated. TRW sites with significant correlations for one of the periods are shown as a function of elevation for <b>b)</b> temperature (15 sites) and <b>d)</b> precipitation (22 sites) with the unfiltered and filtered (see legend and numbers in brackets) data.</p

Evaluations plots for BR:FO.

<p>Upper left: HCA dendrogram suggests selection of four responder chronologies. Note, that cluster four only consists of five specimens. Upper right: <i>grbar</i>_<i>N</i> clearly indicates signal strength enhancement at the margins of the population. Mid left: PCGA extreme responder chronologies show weak correlations with each other. Mid right: PCGA and HCA responder chronology signal correlations are comparably strong: see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158346#pone.0158346.t002" target="_blank">Table 2</a>. Lower left: Same as mid-left but here for the analyses where the minimum sample size was adjusted to match HCA minimum sample size. Lower right: Same as mid right but here for the analyses where the minimum sample size was adjusted to match HCA minimum sample size.</p

Changes in climate from the 1935–1964 to 1982–2011 period spatially interpolated from the climate data (CRU grid point, red dots) closest to the tree-ring sites (black dots) for a) winter (previous year December to current year February, pDJF) and b) summer (June–August; JJA) temperature and c) winter and d) summer precipitation.

<p>Changes in climate from the 1935–1964 to 1982–2011 period spatially interpolated from the climate data (CRU grid point, red dots) closest to the tree-ring sites (black dots) for a) winter (previous year December to current year February, pDJF) and b) summer (June–August; JJA) temperature and c) winter and d) summer precipitation.</p

Evaluation statistics for the Alaskan data.

<p>Evaluation statistics for the Alaskan data.</p

Juniper sampling sites (black dots) and closest CRU grid point data (red dots) in Uzbekistan and Kyrgyzstan.

<p>Westerly winds (A; orange arrow in inset) dominate the study area causing continental climatic conditions at the sites. Climate charts show absolute annual (numbers) and monthly temperature means (red shades) and precipitation sums (blue shades) for all CRU grid points for each region over the period 1961–1990. Monsoonal influences (blue arrows) are depicted for the Indian Summer Monsoon (B) and East Asian Summer Monsoon (C), respectively, while ITCZ stands for Intertropical Convergence Zone (purple line; after Lutgens and Tarbuck, 2001 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153888#pone.0153888.ref031" target="_blank">31</a>]). Topographic features are indicated by digital elevation model data in grey colors.</p

PCGA plots for the Alaskan sites.

<p>PCA loadings coloured according to PCGA gradients for the four Alaskan sites. At each site PCA loadings suggest ecological gradients supported by comparably high explained variances on the first two PCs (values are given for each PC).</p

Evaluation plots for PP1.

<p>Upper panel: <i>grbar</i>_<i>N</i> plotted against along the population gradient for both population margins (red and blue lines). The blue line suggests a slightly higher <i>grbar</i>_<i>N</i> along the gradient. Lower panel: HCA dendrogram suggests defining only one responder chronology, however with weak indications of sub-populations, probably caused by noise similarity. HCA labels indicate to which cluster each RWS belongs (here each belonging to cluster 1), while their colours relate to their position within the known gradient. As no gradient exists within PP1, the colours are mixed randomly.</p

Generation of a heterogeneous pseudo-population.

<p>Two different signals (red and blue) are mixed along a linear gradient (rainbow coloured arrows) to obtain 1000 RWS with a gradual transition of underlying signals. As for PP1 individual-specific white noises are added.</p