Application of EEO Model in Prediction of Peak Vegetation Cover on the Tibetan Plateau

The Tibetan Plateau, known as the “third pole”, has experienced rapid warming in recent years. Despite reports of a general greening across the Tibetan Plateau from remote sensing observations, site and subregional scale studies show a weakening of this trend and even browning responses to warming — challenging the idea that warming has a predominantly positive effect. It thus appears that a unified and quantitative framework is still missing, as previous assumptions inevitably introduce uncertainties in the prediction of future vegetation changes and mitigation strategies in this fragile region.

Ziqi Zhu and coauthors published a new paper named “Optimality Principles Explaining Divergent Responses of Alpine Vegetation to Environmental Change” (see Global Change Biology (, showing progress towards closing the gap in our understanding. The authors propose a theory to investigate patterns of vegetation cover in space and time, by coupling the eco-evolutionary optimality and hydro-climatological rate limitation framework with a universal primary production model (P model). The basic hypothesis is that peak vegetation cover is limited either by energy supply (in which case, allocation to leaves maximises net energy profit) or by water supply (Figure 1).

Figure 1. 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. Fmax refers to the maximum absorbed fraction photosynthetically active radiation. The black and red lines represent annual time series of observed GIMMS Fmax and predicted Fmax in water-limited areas and energy-limited areas over 1982–2016.

This parsimonious modelling framework has succeeded in predicting maximum vegetation cover, with a correlation coefficient (r) of 0.76 and a root mean squared error of 0.12, together with the unexpected spatial divergence of these trends across the Tibetan Plateau (a greening of 0.31±0.14% yr−1 in drier regions and a browning of 0.12±0.08% yr–1 in wetter regions). The analysis 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.

You can download the full paper:  

Zhu, Z., Wang, H., Harrison, S.P., Prentice, I.C., Qiao, S. and Tan, S. 2022. Optimality principles explaining divergent responses of alpine vegetation to environmental change. Global Change Biology 


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