Detecting long‐term growth trends using tree rings: a critical evaluation of methods

Abstract

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends.

No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO 2 fertilization

“…remove long-term growth trends, and by biased sampling of trees that produces spurious trends in growth rates (39,40). To assess the extent to which the choice of approach for removing age, size, and competition effects might influence our conclusions, we additionally applied a tree levelbased statistical procedure, and two uniform ecozone-level procedures to generate three different forms of tree growth indices.…”

“…Our study suggests that elevated atmospheric CO 2 is a large-scale, but subtle phenomenon and the detection of a CO 2 effect requires a large spatial covering where local confounding factors and interannual variability in climate and disturbances average each other out and allow the CO 2 -related effects to be detected. Still, other challenges in attributing the [CO 2 ] factor are worth further exploring and include, among others, the possible alternative pathways for the extra carbon assimilated under increasing [CO 2 ] (36), methodological considerations including collinearity among predictor variables and the standardization of tree-ring data (39), and the impact of sampling design on growth trends (40,41).…”

Differential response of Qilian juniper radial growth to climate variations in the middle of Qilian Mountains and the northeastern Qaidam Basin

“…Nevertheless, the BAI method provides an additional evidence to detect the long-term tree growth trends. A recent study reviewed and evaluated the four different widely used methods to detect long-term tree growth trends and suggested applying multiple methods when analyzing trends (Peters et al 2015). Here, we used both the tree-ring width series deduced from the conservative detrending method and the BAI chronologies to detect the recent Qilian juniper growth trends.…”

Hierarchical regression models for dendroclimatic standardization and climate reconstruction

“…The TS, SF, and RCS methodologies have been discussed at length in the literature (Helama et al, 2004;Peters et al, 2015 and sources within) and the reader is directed to these references for additional detail. We also recognize that numerous other variants of these standardization approaches have been proposed (Briffa et al, 2001;Bontemps and Esper, 2011;Björklund et al, 2013;Briffa et al, 2013;Matskovsky and Helama, 2014;Linderholm et al, 2015;Helama, et al, 2016), but for brevity of exposition we focus on the three strategies discussed above (TS, SF, RCS-SF) and their comparison to a basic hierarchical model to highlight the major methodological and practical differences.…”

“…The RCS approach assumes that the distribution of age classes is sufficiently random across trees in any given time period so that climate-related variance in that time period is averaged out in the calculation of age-related growth for each age class. Because the biological growth curve is estimated using all tree-ring series, it is not constrained by the length of any one series and the resulting chronology can exhibit variability on long timescales up to the length of the full chronology (Esper et al, 2002;Peters et al, 2015).…”