The utility of bulk wood density for tree-ring research

Jesper Björklund,Georg Von Arx,Patrick Fonti,Petter Stridbeck,Tom De Mil,Anna Neycken,Kristina Seftigen

doi:10.1016/j.dendro.2021.125880

Abstract

Bulk wood density measurements are recognized for their utility in ecology, industry, and biomass estimations. In tree-ring research, microdensitometric techniques are widely used, but their ability to determine the correct central tendency has been questioned. Though rarely used, it may be possible to use bulk wood density as a tool to check the accuracy of and even correct microdensitometric measurements. Since measuring bulk wood density in parallel with X-ray densitometry is quickly and easily done, we suspect that its omission is largely due to a lack of awareness of the procedure and/or its importance. In this study, we describe a simple protocol for measuring bulk wood density tailored for tree-ring researchers and demonstrate a few possible applications. To implement real-world examples of the applications, we used a sample of existing X-ray and Blue Intensity (BI) measurements from 127 living and dead Pinus sylvestris trees from northern Sweden to produce new measurements of bulk wood density.We can confirm that the central tendency in this sample material is offset using X-ray densitometry and that the diagnosis and correction of X-ray density is easily done using bulk wood density in linear transfer functions. However, this approach was not suitable for our BI measurements due to heavy discoloration. Nevertheless, we were able to use bulk wood density to diagnose and improve the use of deltaBI (latewood BI – earlywood BI) with regard to its overall trends and multi-centennial variability in a dendroclimatological application. Moreover, we experimented with percent of latewood width, scaled with bulk wood density, as a time- and cost-effective proxy for annual ring density. Although our reconstruction only explains about half of the variation in ring density, it is most likely superior to using fixed literature values of density in allometric equations aimed at biomass estimations.With this study, we hope to raise new awareness regarding the versatility and importance of bulk wood density for dendrochronology by demonstrating its simplicity, relevance, and applicability.

Highlights

Bulk wood density measurements are recognized for their utility in determining optimal end-uses of wood such as for construction or pulp (e.g., Barnett and Jeronimidis, 2003), as multiplier to determine woody biomass from volume estimations of trees and forests (Saranpaa, 2003; Pretzsch et al, 2018), and for understanding the functional and competitive traits of tree species (e.g., Chave et al, 2009)
Our reconstruction only explains about half of the variation in ring density, it is most likely superior to using fixed literature values of density in allometric equations aimed at biomass estimations
We have described a simple measurement protocol for conducting bulk wood density measurements that is tailored for treering researchers

Summary

Introduction

Bulk wood density measurements are recognized for their utility in determining optimal end-uses of wood such as for construction or pulp (e.g., Barnett and Jeronimidis, 2003), as multiplier to determine woody biomass from volume estimations of trees and forests (Saranpaa, 2003; Pretzsch et al, 2018), and for understanding the functional and competitive traits of tree species (e.g., Chave et al, 2009). In tree-ring research, microdensitometric techniques are widely used (Bjorklund et al, 2019). Maximum latewood density (MXD) has become a popular measurement parameter due to its close relationship with past temperatures (George and Esper, 2019), and total ring density (TRD) is increasingly used when deriving annual biomass growth estimates from tree rings (e.g., Babst et al, 2014; Bouriaud et al, 2015; Vannoppen et al, 2017; Zeller et al, 2017). The ability of microdensitometric techniques to reflect the correct central tendency of both TRD and MXD has been questioned (e.g., Bjorklund et al, 2019; Zeller et al, 2017).

Methods

Results

Discussion

Conclusion