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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.
Human-driven peatland drainage has occurred in Europe for centuries, causing habitat degradation and leading to the emission of greenhouse gases. As such, in the last decades, there has been an increase in policies aiming at restoring these habitats through rewetting. Alder (Alnus glutinosa L.) is a widespread species in temperate forest peatlands with a seemingly high waterlogging tolerance. Yet, little is known about its specific response in growth and wood traits relevant for tree functioning when dealing with changing water table levels. In this study, we investigated the effects of rewetting and extreme flooding on alder growth and wood traits in a peatland forest in northern Germany. We took increment cores from several trees at a drained and a rewetted stand and analyzed changes in ring width, wood density, and xylem anatomical traits related to the hydraulic functioning, growth, and mechanical support for the period 1994–2018. This period included both the rewetting action and an extreme flooding event. We additionally used climate-growth and climate-density correlations to identify the stand-specific responses to climatic conditions. Our results showed that alder growth declined after an extreme flooding in the rewetted stand, whereas the opposite occurred in the drained stand. These changes were accompanied by changes in wood traits related to growth (i.e., number of vessels), but not in wood density and hydraulic-related traits. We found poor climate-growth and climate-density correlations, indicating that water table fluctuations have a stronger effect than climate on alder growth. Our results show detrimental effects on the growth of sudden water table changes leading to permanent waterlogging, but little implications for its wood density and hydraulic architecture. Rewetting actions should thus account for the loss of carbon allocation into wood and ensure suitable conditions for alder growth in temperate peatland forests.
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.
Determining the effect of a changing climate on tree growth will ultimately depend on our understanding of wood formation processes and how they can be affected by environmental conditions. In this context, monitoring intra-annual radial growth with high temporal resolution through point dendrometers has often been used. Another widespread approach is the microcoring method to follow xylem and phloem formation at the cellular level. Although both register the same biological process (secondary growth), given the limitations of each method, each delivers specific insights that can be combined to obtain a better picture of the process as a whole. To explore the potential of visualizing combined dendrometer and histological monitoring data and scrutinize intra-annual growth data on both dimensions (dendrometer → continuous; microcoring → discrete), we developed DevX (Dendrometer vs. Xylogenesis), a visualization application using the “Shiny” package in the R programming language. The interactive visualization allows the display of dendrometer curves and the overlay of commonly used growth model fits (Gompertz and Weibull) as well as the calculation of wood phenology estimates based on these fits (growth onset, growth cessation, and duration). Furthermore, the growth curves have interactive points to show the corresponding histological section, where the amount and development stage of the tissues at that particular time point can be observed. This allows to see the agreement of dendrometer derived phenology and the development status at the cellular level, and by this help disentangle shrinkage and swelling due to water uptake from actual radial growth. We present a case study with monitoring data for Acer pseudoplatanus L., Fagus sylvatica L., and Quercus robur L. trees growing in a mixed stand in northeastern Germany. The presented application is an example of the innovative and easy to access use of programming languages as basis for data visualization, and can be further used as a learning tool in the topic of wood formation and its ecology. Combining continuous dendrometer data with the discrete information from histological-sections provides a tool to identify active periods of wood formation from dendrometer series (calibrate) and explore monitoring datasets.