Processing and extraction of seasonal tree physiological parameters from stem radius time series

Abstract

Radial stem size changes, measured with automated dendrometers at intra-daily resolution, offer great potential to link environmental conditions with tree physiology at the seasonal scale. Such measurements need to be time-aligned, cleaned of outliers and shifts, gap-filled and analysed for reversible (water-related) and irreversible (growth-related) fractions to obtain physiologically meaningful data. Therefore, comprehensive tools are needed for reproducible data processing and analytics of dendrometer data. Here we present a transparent method, compiled in the R package treenetproc, to turn raw dendrometer data into clean, physiologically interpretable information, i.e., stem growth, tree water deficit, growth phenological phases, mean daily shrinkage and their respec-tive timings. The removal of errors is facilitated by additional functions and supported with graphical visualizations. To ensure reproducible data handling, the processing parameters and induced changes to the raw data are documented in the output and, thus, are a step towards a standardized processing of automatically measured stem radius time series. We discuss examples, such as the seasonality of growth or the dependence of growth on atmospheric and soil drought. The presented growth and water-related physiological variables at high temporal resolution offer novel physiological insights into the seasonally varying responses of trees to changing environmental conditions.