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Changes in the environment will alter the growth rate of trees and forests. Different disciplines assess such growth rates differently, for example, with tree-ring width data, forest inventories or with carbon-flux data from eddy covariance towers. Such data is used to quantify forests biomass increment, forest’s carbon sequestration or to reconstruct environmental variables before instrumental records. However, raw measurement data is typically not considered to be representative for the average growth rate of trees or forests. Depending on the research question, the effects of certain environmental variables or effects of tree and forest structure have to be removed first. It can be challenging to define and quantify a growth trend that can answer a specific research question because trees and forests grow and respond to environmental change in multiple ways simultaneously, for example, with altered radial increment, height growth, and stand density. Further challenges pose time-lagged feedback loops, for example, between height and radial increment or between stand density and radial increment. Generally, different environments will lead to different tree and forest structures, but because of tree’s longevity this adaptation to the new environment will take decades or even centuries. Consequently, there can be an offset between the present forest structure and what we term the potential natural forest (PNF): Similar to the potential natural vegetation (PNV), the PNF represents that forest that would develop under the current environmental conditions in the absence of human intervention. Because growth rates are affected by the tree and forest structure, growth-trend estimates will differ between the present and the potential forest. Consequently, if the legacy effects of the past are not of interest, the PNF is the theoretical baseline to correct and estimate growth trends.
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.