Early-Warning Signals of Individual Tree Mortality Based on Annual Radial Growth

Abstract

Tree mortality is a key driver of forest dynamics and its occurrence is projected to increase in the future due to climate change. Despite recent advances in our understanding of the physiological mechanisms leading to death, we still lack robust indicators of mortality risk that could be applied at the individual tree scale. Here, we build on a previous contribution exploring the differences in growth level between trees that died and survived a given mortality event to assess whether changes in temporal autocorrelation, variance, and synchrony in time-series of annual radial growth data can be used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and 4389 living trees growing together at 198 sites (belonging to 36 gymnosperm and angiosperm species), we analyzed temporal changes in autocorrelation, variance, and synchrony before tree death (diachronic analysis), and also compared these metrics between trees that died and trees that survived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in inter-annual growth variability and a decrease in growth synchrony in the last ∼20 years before mortality of gymnosperms, irrespective of the cause of mortality. These changes could be associated with drought-induced alterations in carbon economy and allocation patterns. In angiosperms, we did not find any consistent changes in any metric. Such lack of any signal might be explained by the relatively high capacity of angiosperms to recover after a stress-induced growth decline. Our analysis provides a robust method for estimating early-warning signals of tree mortality based on annual growth data. In addition to the frequently reported decrease in growth rates, an increase in inter-annual growth variability and a decrease in growth synchrony may be powerful predictors of gymnosperm mortality risk, but not necessarily so for angiosperms.

Keywords: biotic agents; drought; forest; growth; resilience indicators; ring-width; tree mortality; variance.

FIGURE 1

Example of early-warning signals of tree mortality based on ring-width (RW) data from two Abies alba trees from Mont Ventoux, France (Cailleret et al., 2014). The Standard Deviation (SD), first-order autocorrelation (AR1) and Pearson correlation coefficients (COR) were calculated on the original (Left) and detrended (Right) RW data using 20-year moving time windows.

FIGURE 2

Temporal change in SD₂₀ (A), AR1₂₀ (B), and COR₂₀ (C) before death averaged for all dying trees and calculated on the original and detrended RW data. We also show the temporal change in the residuals of the linear mixed-effects models fitted to these metrics (right y-axes). Shaded areas represent the 95% confidence intervals of the means. Note that COR20 values were not calculated on not-detrended RW data.

FIGURE 3

Variation in the residuals of SD (A), AR1 (B), and COR (C) calculated over the last 20-year period of the detrended ring-width time series preceding tree death (resSD_20f, resAR1_20f, and resCOR_20f) among mortality sources and species groups. Error bars depict 95% confidence intervals of the mean residuals, which were determined from 500 bootstrap resamplings of the original dataset.

FIGURE 4

Mean difference in SD_20f (A,B), AR1_20f (C,D), and COR_20f (E,F) values between dying and surviving trees predicted by the linear mixed-effects models (LMMs) fitted to the original dataset, fixing diff_D-SRW_20f and diff_D-SDBH_f at zero. Positive values mean that dying trees showed higher SD_20f, AR1_20f, or COR_20f compared to conspecific surviving trees. Standardization was based on similar meanRW_20f (Left) and similar DBH_f (Right). Error bars depict 95% confidence intervals of the predicted mean differences, which were determined from 500 bootstrap resamplings. Estimates of the LMMs are available in Supplementary Table H1.