Recurrent extreme climate events shift stomatal regulation while maintaining carbon functioning in surviving Larix kaempferi seedlings

Extreme heat and precipitation are becoming more frequent and threaten forest regeneration success by reducing seedling carbon assimilation and growth. However, it remains unclear whether seedlings physiologically acclimate or experience cumulative damage when extreme climate events recur across consecutive years. To address this, we investigated the effects of recurrent summer extreme climates on the photosynthetic performance, growth, and belowground carbon dynamics of Larix kaempferi seedlings using an open-field manipulation experiment. 1-year-old seedlings were exposed to a factorial combination of three temperature treatments (ambient, moderate heat [+3 °C], and extreme heat [+6 °C]) and three precipitation treatments (reduced, ambient, and heavy rainfall) over two consecutive summers. In the first year, extreme heat reduced stomatal conductance, resulting in a 13% decrease in net photosynthetic rate (A) and a fourfold increase in mortality. In contrast, reduced rainfall enhanced maximum carboxylation capacity and A by 41% and 18%, respectively, compared with ambient rainfall, indicating that lower water inputs improved photosynthetic performance under naturally wet conditions. When the stresses recurred in the following year, the treatments no longer affected absolute photosynthetic performance. Under recurrent extreme heat, stomatal regulation shifted toward a less water-conservative strategy, as indicated by lower water-use efficiency and greater-than-modeled stomatal opening in the second year than in the first. Seedling biomass production, biomass allocation, and soil CO2 efflux remained similar among treatments in the second year. The maintenance of leaf-level carbon assimilation and whole-seedling carbon functioning suggests physiological adjustments of surviving L. kaempferi seedlings under recurrent stresses. Our results highlight that regeneration projections should account for climate history and stress legacies to better predict forest regeneration under increasingly recurrent climate extremes.