Abstract
Traditional photosynthesis-driven growth models have considerable uncertainties in predicting tree growth under changing climates, partially because sink activities are directly affected by the environment but not adequately addressed in growth modelling. Therefore, we developed a semi-mechanistic model coupling stomatal optimality, temperature control of enzymatic activities and phenology of cambial growth. Parameterized using Bayesian inference and measured data on Picea abies and Pinus sylvestris in peatland and mineral soils in Finland, the coupled model simulates transpiration and assimilation rates and stem radial dimension (SRD) simultaneously at 30 min resolution. The results suggest that both the sink and phenological formulations with environmental effects are indispensable for capturing SRD dynamics across hourly to seasonal scales. Simulated using the model, growth was more sensitive than assimilation to temperature and soil water, suggesting carbon gain is not driving growth at the current temporal scale. Also, leaf-specific production was occasionally positively correlated with growth duration but not with growth onset timing or annual cambial area increment. Thus, as it is hardly explained by carbon gain, phenology itself should be included in sink-driven growth models of the trees in the boreal zone and possibly other environments where sink activities and photosynthesis are both restrained by harsh conditions.
| Original language | English |
|---|---|
| Journal | Plant Cell and Environment |
| Volume | 48 |
| Issue number | 2 |
| Pages (from-to) | 1344-1365 |
| Number of pages | 22 |
| ISSN | 0140-7791 |
| DOIs | |
| Publication status | Published - Feb 2025 |
| MoE publication type | A1 Journal article-refereed |
Fields of Science
- cambial growth
- eco-physiological modelling
- photosynthesis
- sink activity
- soil water
- stomatal behaviour
- temperature
- transpiration
- 4112 Forestry
- 1181 Ecology, evolutionary biology
- 11831 Plant biology
- 114 Physical sciences
- 1171 Geosciences
- 1172 Environmental sciences