Improving leaf spring phenology modelling for temperate tree species: An integration of the Farquhar–Medlyn photosynthesis model with the optimality‐based approach

作者：Yating Gu, Zefei Wu, Matteo Detto, Dedi Yang, Jiayue Wang, Yingyi Zhao, Xi Yang, Jin Wu · 发表于：Methods in Ecology and Evolution · 年份：2025 · DOI：10.1111/2041-210x.70181 · 被引用次数：1 · 研究领域：Plant Water Relations and Carbon Dynamics、Remote Sensing in Agriculture、Plant responses to elevated CO2

Abstract Spring leaf phenology in temperate tree species is highly sensitive to climate change and significantly affects plant photosynthetic performance, resource utilization, competition and trophic interactions, thereby impacting various ecosystem functions. Although optimality‐based (OPT) approaches for modelling spring phenology are increasingly recognized, the optimal representation of the underlying principle (balancing photosynthesis gains with chilling risks) remains controversial. Here, we integrated a coupled Farquhar–Medlyn photosynthesis model into an existing OPT model, and termed the resulting model R‐OPT, and evaluated its performance using the PEP725 dataset, which includes 409,144 site‐species‐year records from across Europe. Our results show that R‐OPT outperforms both the default OPT and non‐optimality‐based models (e.g. the chilling‐forcing trade‐off and growing degree day models). This improved performance is consistent within and across five focal tree species but varies by region: R‐OPT excels in lowland, moist environments but is less effective in high‐altitude, cold, and dry areas, possibly due to an incomplete representation of environmental constraints on photosynthetic carbon gain in these regions. Our research advances leaf spring phenology modelling by emphasizing an optimality principle that balances photosynthetic carbon gain with chilling risk, improving the representation of plant photosynthesis processes and enhancing understanding of envir...