Simulation of forest tree species’ bud burst dates for different climate scenarios: chilling requirements and photo-period may limit bud burst advancement

Abstract

This study investigates whether the assumed increase of winter and spring temperatures is depicted by phenological models in correspondingly earlier bud burst (BB) dates. Some studies assume that rising temperatures lead to an earlier BB, but even later BB has been detected. The phenological model PIM (promoter-inhibitor-model) fitted to the extensive phenological database of the German Weather Service was driven by several climate scenarios. This model accounts for the complicated mechanistic interactions between chilling requirements, temperature and photo-period. It predicts BB with a r ² between 0.41 and 0.62 and a RMSE of around 1 week, depending on species. Parameter sensitivities depict species dependent interactions between growth and chilling requirements as well as photo-period. A mean trend to earlier BB was revealed for the period 2002– 2100, varying between −0.05 and −0.11 days per year, depending on species. These trends are lower than for the period 1951– 2009. Within climate scenario period, trends are decreasing for beech and chestnut, stagnating for birch and increasing for oak. Results suggest that not fulfilled chilling requirements accompanied by an increasing dependency on photo-period potentially limit future BB advancement. The combination of a powerful phenological model, a large scale phenological database and several climate scenarios, offers new insights into the mechanistic comprehension of spring phenology.

Appendices

Appendix A: Selected promoter-inhibitor-models

The best model for each selected species was chosen, therefore four models were used. For species birch (B. pendula), model number two with a ₁ = 0 and no photo-period dependence for the forcing term of promoter and the breakdown term of inhibitor was used:

$$\begin{array}{@{}rcl@{}} {\Delta} I& =& - a_2\ast f_2(T)\ast I\\ {\Delta} P &=& a_3\ast f_3(T)\ast(1-I)\\ &&- a_4\ast\frac{24-L}{24}\ast P \end{array} $$

(9)

For species chestnut, model number eight with a ₁ = 0 and no photo-period dependence for the forcing term of promoter was used:

$$\begin{array}{@{}rcl@{}} {\Delta} I &=& - a_2\ast f_2(T)\ast\frac{L}{24}\ast I\\ {\Delta} P &=& a_3\ast f_3(T)\ast(1-I)\\ &&- a_4\ast\frac{24-L}{24}\ast P \end{array} $$

(10)

For species beech, model number eleven with a ₁ = 0 was used:

$$\begin{array}{@{}rcl@{}} {\Delta} I &=& - a_2\ast f_2(T)\ast\frac{L}{24}\ast I\\ {\Delta} P &=& a_3\ast f_3(T)\ast(1-I)\ast \frac{L}{24}\\ &&- a_4\ast \frac{24-L}{24}\ast P \end{array} $$

(11)

For species oak, model number twelve with a ₄ = 0 was used:

$$\begin{array}{@{}rcl@{}} {\Delta} I &=& a_1\ast\frac{24-L}{24} - a_2\ast f_2(T)\ast\frac{L}{24}\ast I\\ {\Delta} P &=& a_3\ast f_3(T)\ast(1-I)\ast\frac{L}{24} \end{array} $$

(12)

Appendix B: DEoptim parameter settings

The optimization with DEoptim was done with following parameters:

• population size N P = 400

• iteration number I t = 100

• crossover rate C R = 0.9

These parameters determine the fits’ convergence behaviour. The parameter bounds were set to following values:

• −24^∘ to 32^∘ Celsius for temperature thresholds

• 0 to 1 for scaling parameters a _i