Optimality principles explaining divergent responses of alpine vegetation to environmental change. Issue 1 (17th October 2022) - Zhu, Ziqi; Wang, Han; Harrison, Sandy P.; Prentice, Iain Colin; Qiao, Shengchao; Tan, Shen
Abstract: Recent increases in vegetation greenness over much of the world reflect increasing CO2 globally and warming in cold areas. However, the strength of the response to both CO2 and warming in those areas appears to be declining for unclear reasons, contributing to large uncertainties in predicting how vegetation will respond to future global changes. Here, we investigated the changes of satellite‐observed peak season absorbed photosynthetically active radiation ( F max ) on the Tibetan Plateau between 1982 and 2016. Although climate trends are similar across the Plateau, we identified robust divergent responses (a greening of 0.31 ± 0.14% year −1 in drier regions and a browning of 0.12 ± 0.08% year −1 in wetter regions). Using an eco‐evolutionary optimality (EEO) concept of plant acclimation/adaptation, we propose a parsimonious modelling framework that quantitatively explains these changes in terms of water and energy limitations. Our model captured the variations in F max with a correlation coefficient ( r ) of .76 and a root mean squared error of .12 and predicted the divergent trends of greening (0.32 ± 0.19% year −1 ) and browning (0.07 ± 0.06% year −1 ). We also predicted the observed reduced sensitivities of F max to precipitation and temperature. The model allows us to explain these changes: Enhanced growing season cumulative radiation has opposite effects on water use and energy uptake. Increased precipitation has an overwhelmingly positive effect in drier regions, whereas warming reduces F max in wetter regions by increasing the cost of building and maintaining leaf area. Rising CO2 stimulates vegetation growth by enhancing water‐use efficiency, but its effect on photosynthesis saturates. The large decrease in the sensitivity of vegetation to climate reflects a shift from water to energy limitation. Our study demonstrates the potential of EEO approaches to reveal the mechanisms underlying recent trends in vegetation greenness and provides further insight into the response of alpine ecosystems to ongoing climate change. Abstract : A new water supply constraint eco‐evolutionary optimality (EEO) model can successfully predict the divergent trend of leaf area index on the Tibetan Plateau over the past 35 years. Our model accounts for EEO of carbon allocation to leaves, subject to constraints by water availability. F max refers to the maximum absorbed fraction photosynthetically active radiation. The black and red lines represent annual time series of observed GIMMS F max and predicted F max in water‐limited areas and energy‐limited areas over 1982–2016.