Asynchronous leaf and cambial phenology in a tree species of the Congo Basin requires space-time conversion of wood traits

Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937-1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate-phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals.

Cutting tree rings into time slices: how intra-annual dynamics of wood formation help decipher the space-for-time conversion

Tree-ring anatomy, microdensity and isotope records provide valuable intra-annual information. However, extracting signals at that scale is challenged by the complexity of xylogenesis, where two major processes - cell enlargement and wall thickening - occur at different times and rates. We characterized the space-for-time association in the tree rings of three conifer species by examining the duration, overlapping, inter-tree synchronicity and interannual stability during cell enlargement and wall thickening across regular tree-ring sectors (portions of equal tangential width). The number of cells and cell differentiation rates determined the duration of sector formation, which augmented more rapidly throughout the ring for wall thickening than for enlargement. Increasing the number of sectors above c. 15 had a limited effect on improving time resolution because consecutive sector formation overlapped greatly in time, especially in narrow rings and during wall thickening. Increasing the number of sectors also resulted in lower synchronicity and stability of intermediate-sector enlargement, whereas all sectors showed high synchronicity and stability during wall thickening. Increasing the number of sectors had a stronger effect on enhancing time-series resolution for enlargement- than for wall-thickening-related traits, which would nevertheless produce more reliable intra-annual chronologies as a result of the more similar calendars across trees and years in wall thickening.

Processing and Extraction of Seasonal Tree Physiological Parameters from Stem Radius Time Series

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 respective 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.

Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation

To maximize utilization of our forest resources, detailed knowledge of wood property variation and the impacts this has on end-product performance is required at multiple scales (within and among trees, regionally). As many wood properties are difficult and time-consuming to measure our knowledge regarding their variation is often inadequate as is our understanding of their responses to genetic and silvicultural manipulation. The emergence of many non-destructive evaluation (NDE) methodologies offers the potential to greatly enhance our understanding of the forest resource; however, it is critical to recognize that any technique has its limitations and it is important to select the appropriate technique for a given application. In this review, we will discuss the following technologies for assessing wood properties both in the field: acoustics, Pilodyn, Resistograph and Rigidimeter and the lab: computer tomography (CT) scanning, DiscBot, near infrared (NIR) spectroscopy, radial sample acoustics and SilviScan. We will discuss these techniques, explore their utilization, and list applications that best suit each methodology. As an end goal, NDE technologies will help researchers worldwide characterize wood properties, develop accurate models for prediction, and utilize field equipment that can validate the predictions. The continued advancement of NDE technologies will also allow researchers to better understand the impact on wood properties on product performance.