Early-warning signals of individual tree mortality based on annual radial growth

Tree mortality is a key driver of forest dynamics and its occurrence is projectedto increase in the future due to climate change. Despite recent advances in ourunderstanding 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 canbe used as early warning signals of mortality risk. Taking advantage of a unique global ring-width database of 3065 dead trees and more than 4 thausands living trees growing together at de i hundred sites (belonging to thirtysix gymnosperm and angiosperm species), we analyzed temporalchanges in autocorrelation, variance, and synchrony before tree death (diachronicanalysis), and also compared these metrics between trees that died and trees thatsurvived a given mortality event (synchronic analysis). Changes in autocorrelation were a poor indicator of mortality risk. However, we found a gradual increase in interannual growth variability and a decrease in growth synchrony in the last twenty 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-annualgrowth variability and a decrease in growth synchrony may be powerful predictors ofgymnosperm mortality risk, but not necessarily so for angiosperms.