Altered microclimatic conditions and higher disturbance at forest edges create environmental stress and modify resource gradients from edge to interior, changing the selection pressures acting on individuals. Although community-weighted trait-mean (CWM) shifts along edge gradients have been widely documented at the species level, it is unclear how edge effects act at the individual level, and whether the direction of intraspecific trait shifts mirrors that of CWM shifts in response to edge effects. On 20 islands in the Thousand Island Lake, China, we established 484 plots (2×2 m) in a stratified random design across distances of 0 – 128 m from the forest edge. Within each plot, we sampled leaves (n=34,768) from within and among all 2,993 individuals of 68 species and measured five leaf traits (leaf area, LA; specific leaf area, SLA; leaf dry matter content, LDMC; thickness, LT; chlorophyll content, LCC). Using generalized linear mixed models, we found that different leaf traits exhibited contrasting shifts in inter- vs. intraspecific trait variation in response to edge effects. For SLA, LT, and LCC, negative covariance between inter- and intraspecific trait shifts resulted in dampening of community-wide trends compared to CWM response to edge effects. In contrast, the community-wide trend for LDMC was reinforced due to positive covariance between inter- and intraspecific trait shifts, while for LA the direction of covariance shifted from negative to positive on small vs. large islands. Together, edge effects alter selection regimes in reassembling plant communities. Predicting the community-wide consequences depends on the degree to which there is negative vs. positive covariance between species sorting and within-species adaptation. The widely-used CWM approach can mask contrasting trait selection pressures acting on individuals within local populations. Individual-level trait variation can improve understanding of community re-assembly trajectories in response to global environmental change.
|Date made available||2022|