Functional traits and climate drive interspecific differences in disturbance‐induced tree mortality

Abstract

With climate change, natural disturbances such as storm or fire are reshuffled, inducing pervasive shifts in forest dynamics. To predict how it will impact forest structure and composition, it is crucial to understand how tree species differ in their sensitivity to disturbances. In this study, we investigated how functional traits and species mean climate affect their sensitivity to disturbances while controlling for tree size and stand structure. With data on 130,594 trees located on 7617 plots that were disturbed by storm, fire, snow, biotic or other disturbances from the French, Spanish, and Finnish National Forest Inventory, we modeled annual mortality probability for 40 European tree species as a function of tree size, dominance status, disturbance type, and intensity. We tested the correlation of our estimated species probability of disturbance mortality with their traits and their mean climate niches. We found that different trait combinations controlled species sensitivity to disturbances. Storm‐sensitive species had a high height‐dbh ratio, low wood density and high maximum growth, while fire‐sensitive species had low bark thickness and high P50. Species from warmer and drier climates, where fires are more frequent, were more resistant to fire. The ranking in disturbance sensitivity between species was overall consistent across disturbance types. Productive conifer species were the most disturbance sensitive, while Mediterranean oaks were the least disturbance sensitive. Our study identified key relations between species functional traits and disturbance sensitivity, that allows more reliable predictions of how changing climate and disturbance regimes will impact future forest structure and species composition at large spatial scales.

Competition‐induced tree mortality across Europe is driven by shade tolerance, proportion of conspecifics and drought

“…This is particularly the case for Picea abies (L.), which is the most sensitive to bark beetles—a currently dominant biotic disturbance agent in Central Europe (Kautz et al, 2017)—and other species susceptible to biotic disturbances, such as Betula spp. (Barrere et al, 2023). Trees already under stress due to suppression are most susceptible to the influence of these processes, which may ultimately lead to tree death (Franklin et al, 1987).…”

“…Other studies have also found an increasing probability of mortality for large trees, mainly due to senescence (Holzwarth et al, 2013;Vieilledent et al, 2010) and certain natural disturbances such as high winds (Barrere et al, 2023). Mortality due to senescence occurs in stands that have avoided natural or anthropogenic disturbances long enough for the trees to reach the end of their life span (Luo & Chen, 2011).…”

“…( Barrere et al, 2023). Trees already under stress due to suppression are most susceptible to the influence of these processes, which may ultimately lead to tree death (Franklin et al, 1987).…”

Damage to living trees contributes to almost half of the biomass losses in tropical forests

“…This included the death of three of the biggest trees (diameter > 100 cm) in the plot that died without signs of prior damage. Whether specific disturbances (e.g., fire, droughts, insect outbreaks; Barrere et al, 2023) and/or sustained changes in climatic factors (e.g., atmospheric water stress; Bauman et al, 2022) are driving these patterns remains unclear, but we found no relationship between damage-related and mortality-related AGB loss rates among periods (even when excluding Fushan and Pasoh) (Figure S5; p > .10…”

“…This included the death of three of the biggest trees (diameter > 100 cm) in the plot that died without signs of prior damage. Whether specific disturbances (e.g., fire, droughts, insect outbreaks; Barrere et al, 2023) and/or sustained changes in climatic factors (e.g., atmospheric water stress; Bauman et al, 2022) are driving these patterns remains unclear, but we found no relationship between damage‐related and mortality‐related AGB loss rates among periods (even when excluding Fushan and Pasoh) (Figure S5; p > .10 in linear mixed‐effect models controlling for temporal autocorrelation within sites) suggesting that these two sources of AGB loss may either result from different drivers or operate at different timescales (i.e., lagged effects). As data collection continues, comprehensive analyses of the underlying drivers of tree mortality and damage will be possible.…”