To extract or not to extract? Influence of chemical extraction treatment of wood samples on element concentrations in tree-rings measured by X-ray fluorescence

In micro-densitometry of wood it is standard procedure to extract resin and other soluble compounds before X-ray analysis to eliminate the influence of these extractives on wood-density. Dendrochemical studies using X-ray fluorescence analysis on the other hand are commonly conducted without previous extraction. However, it is well known that translocation processes of elements during heartwood formation in trees or (temporal) differences in sap content of wood samples can influence dendrochemical element profiles. This might bias environmental signals stored in time series of element concentrations in wood proxies. We hypothesize that metals tightly bound to cell walls show a more robust proxy potential for environmental conditions than easily translocated ones. To eliminate the noise of these soluble substances in wood elemental time series, their extraction prior to analysis might be necessary. In our study we tested the effect of different solvents (water, alcohol, and acetone) and different extraction times on elemental time series of three tree species with differing wood structure (Pinus sylvestris; Quercus robur and Populus tremula). Micro-XRF analysis was conducted on nine replicates per species using an ITRAX-Multiscanner. A set of elements commonly detected in wood (S, Cl, K, Ca, Ti, Mn, Fe, and Ni) was analysed at high resolution before and after several extraction runs. Besides lowering their levels, extraction did not significantly change the temporal trends for most elements. However, for some elements, e.g., Potassium, Chlorine or Manganese, especially the water extraction led to significant decreases in concentrations and altered temporal trends. Apparently the dipole effect of water produced the strongest extraction power of all three solvents. In addition we observed a dependency of extraction intensity from wood density which differed between wood types. Our results help in interpreting and evaluating element profiles and mark a step forward in establishing dendrochemistry as a robust proxy in dendro-environmental research

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

Generation of a homogeneous pseudo-population.

<p>From one Scots Pine ring-width series, 1000 pseudo-trees are generated by adding 1000 different white noises to this particular series.</p

Long term signals of the pseudo-populations.

<p>The signals behind PPs 2–4 showing no (PP2) or differing long-term trends (PP3-4). To better visualize the differing long-term trends these were multiplied by factor 3.</p

Evaluation plots for PP 4_x.

<p>For detailed explanations we refer to the caption of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158346#pone.0158346.g005" target="_blank">Fig 5</a>.</p

Evaluation plots for PP 2.

<p>(for a higher resolved image we refer to supplement <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158346#pone.0158346.s001" target="_blank">S1 Fig</a>). Upper left panel: Loadings of the PCA over RWS coloured according to the PCGA gradient. Axis labels refer to PC relative importance. Three upper right panels: Detected gradients plotted against known gradients for PCGA, GR, and TR, respectively. Headers of these panels reflect the correlation coefficient between detected and true gradient. Mid left: <i>grbar</i>_<i>N</i> plotted against along the population gradient for both population margins (red and blue lines). Both <i>grbar</i>_<i>N</i> strongly suggest a population gradient as being well above . Mid right: HCA dendrogram suggested defining two responder chronologies which mostly correctly split RWS along the known gradient. Lower left: PCGA responder chronologies (coloured curves) show a minimum correlation of 0.74 among each other. Two lower right panels: RWS signal correlations (small dots coloured according to PCGA gradient) show a clear relationship with PCGA gradient. PCGA (coloured ‘◊’) and HCA (black ‘X’) responder chronologies show equal signal correlations of which the marginal responder chronologies express higher correlations than the population master (grey ‘+’).</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

Evaluation statistics for the Alaskan data.

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