Phenological response to climate variation in a northern red oak plantation: Links to survival and productivity

Abstract

In a changing climate, the future survival and productivity of species rely on individual populations to respond to shifting environmental conditions. Many tree species, including northern red oak (Quercus rubra), exhibit phenotypic plasticity, the ability to respond to changes in environmental conditions at within-generation time scales, through varying traits such as leaf phenology.Phenotypic plasticity of phenology may vary among populations within a species' range, and it is unclear if the range of plasticity is adequate to promote fitness. Here, we used a 58-year-old common garden to test whether northern red oak populations differed in phenological sensitivity to changes in temperature and whether differences in phenological sensitivity were associated with differences in productivity and survival (proxies of fitness). We recorded 8 years of spring leaf emergence and autumn leaf coloration and loss in 28 distinct populations from across the species' full range. Across the 28 populations, spring leaf out consistently advanced in warmer years, but fall phenology was less responsive to changes in temperature. Southern, warm-adapted populations had larger shifts in phenology in response to springtime warming but had lower long-term survival. Moreover, higher phenological sensitivity to spring warming was not strongly linked to increased productivity. Instead, fitness was more closely linked to latitudinal gradients. Although springtime phenological sensitivity to climate change is common across northern red oak populations, responses of productivity and survival, which could determine longer-term trajectories of species abundance, are more variable across the species' range.

Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change

“…For instance, while spring phenology tends to advance with climate warming, changes in autumn phenology exhibit heterogeneity (18,56). The complexity of factors influencing autumn phenology, such as temperature, photoperiod, CO 2 , early phenological events, and carbohydrate accumulation, makes its prediction challenging (10,32,51,(57)(58)(59). However, incorporating the constant time allocation pattern into phenology models is expected to enhance predictive capabilities.…”