Vegetation phenology refers to the study of recurring events in the plant life cycle (Grossman 2023). It has gained increasing attention due to its significant implications for ecosystem functioning and climate change (Piao et al., 2019). Accurately modeling vegetation phenology is essential for advancing our understanding of the responses of plants plant response to changing environmental conditions and for predicting future changes in ecosystem dynamics and productivity.
The phenology model builds on the foundation of plant physiological processes and quantifies the impact of external environmental factors on plant phenology. Through statistical analysis and process simulations, mathematical equations grounded in the phenological process are constructed (Piao et al., 2019).
Statistical models are often based on regression analysis, and use the timing of past phenological events and climate variables such as temperature and soil moisture to predict phenological events. These models do not attempt to simulate the underlying physiological processes, but instead rely on empirical relationships between climate variables and phenology.
By contrast, process-based models simulate the physiological processes that control phenology, such as photosynthesis and respiration, and use these processes to predict the timing of phenological events (Walker et al., 2014). These models require detailed information about the structure and allocation strategy of the plant, as well as environmental variables such as temperature and light.
By and large, both statistical and process-based models can perform well in specific biomes in predicting phenology, yet each has its advantages and disadvantages. Statistical models are relatively simple and require less data than process-based models, but they may not be as accurate in predicting phenology under changing environmental conditions. Process-based models, on the other hand, are more complex and require more data, but they are more flexible in their ability to simulate phenology under changing environmental conditions. However, the processed phenology model does have limitations when it comes to predicting global vegetation phenology in diverse biomes. The potential reason for the relatively poor performance is that yet it is still largely empirical so far (Richardson et al., 2010).
We are developing a parsimonious time-stepping scheme, combining with Eco-evolutionary optimality theory (EEO) theory, to simulate leaf area index and phenology when forced with environmental variables. As such, a linear function between steady-state LAI and corresponding GPP is established. The developed time-stepping scheme provides a simplified and robust version of modeling approach to simulate leaf phenology, that may be applied globally. Stay tuned for our findings as we explore solutions based on this new approach.
Grossman, J. J. Phenological physiology: seasonal patterns of plant stress tolerance in a changing climate. New Phytol. 237, 1508-1524 (2023). https://doi.org:10.1111/nph.18617
Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Chang. Biol. 25, 1922-1940 (2019). https://doi.org:10.1111/gcb.14619
Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Global Change Biology 18, 566-584 (2012). https://doi.org:10.1111/j.1365-2486.2011.02562.x
Walker, A. P. et al. Comprehensive ecosystem model-data synthesis using multiple data sets at two temperate forest free-air CO2enrichment experiments: Model performance at ambient CO2concentration. J. Geophys. Res. Biogeosci. 119, 937-964 (2014). https://doi.org:10.1002/2013jg002553