• Energy Research

Understanding how climate warming has an impact on the life cycle schedule of terrestrial organisms is critical to evaluate ecosystem vulnerability to environmental change. Despite recent advances identifying the molecular basis of temperature responses, few studies have incorporated this knowledge into predictive models. Here we develop a method to forecast flowering phenology by modelling regulatory dynamics of key flowering-time genes in perennial life cycles. The model, parameterized by controlled laboratory experiments, accurately reproduces the seasonal changes in gene expression, the corresponding timing of floral initiation and return to vegetative growth after a period of flowering in complex natural environments. A striking scenario forecast by the model under climate warming is that the shift in the return time to vegetative growth is greater than that in floral initiation, which results in a significant reduction of the flowering period. Our study demonstrates the usefulness of gene expression assessment to predict unexplored risks of climate change.

Understanding how climate warming has an impact on the life cycle schedule of terrestrial organisms is critical to evaluate ecosystem vulnerability to environmental change. Despite recent advances identifying the molecular basis of temperature responses, few studies have incorporated this knowledge into predictive models. Here we develop a method to forecast flowering phenology by modelling regulatory dynamics of key flowering-time genes in perennial life cycles. The model, parameterized by controlled laboratory experiments, accurately reproduces the seasonal changes in gene expression, the corresponding timing of floral initiation and return to vegetative growth after a period of flowering in complex natural environments. A striking scenario forecast by the model under climate warming is that the shift in the return time to vegetative growth is greater than that in floral initiation, which results in a significant reduction of the flowering period. Our study demonstrates the usefulness of gene expression assessment to predict unexplored risks of climate change.