I have been interested in plant hydraulics since I was an undergraduate. After I joined LPICEA at Tsinghua University, my supervisor Han Wang gave me the chance to learn about and measure hydraulic traits in the field. Thus, I tried to combine my interest with Han’s expertise in photosynthesis, which led to this paper. Our paper, with Colin and Sandy, analysing the coordination of plant hydraulic and photosynthetic traits, using field data we collected in the Gongga Mountains in China from 2018 to 2019 and the application of eco-evolutionary optimality theory, has now been accepted for publication in New Phytologist (doi: 10.1111/nph.17656).

Close coupling between water loss and CO2 uptake processes lead to the long-observed coordination between hydraulic and photosynthetic traits in the field. However, their relationships have not been fully quantitatively understood, which hinders the improvement of estimation of carbon and water cycle in land surface models, especially under drought conditions. Recently, eco-evolutionary optimality allows us to comprehend the coordination between photosynthetic and hydraulic traits quantitatively. We developed a simple model to predict a key trait linking photosynthesis, leaf economics spectrum and hydraulics – the Huber value, which builds on the hypothesis that water transport through the xylem must equal water loss via the stomata. Our study showed that the model could capture 90% of Huber value variation across sites and explain the trait-trait relationship from a fundamental perspective. Besides, the model could also predict Huber value theoretical response to climate variables. This research suggests a route towards the integration of photosynthesis and hydraulics in land-surface models.

Fig. 1 Comparison between site-mean observed and predicted Huber value (vH).

This work provides another great example of how it is possible to make theoretical trait prediction using trait data gathered in the field with the application of eco-evolutionary optimality principles — which makes the many hours work put in by many people in collecting these data very worthwhile. My thanks to all of those who measured traits in the field and laboratory (Yuechen Chu, Yingying Ji, Sandy Harrison, Meng Li, Xinyu Liu, Giulia Mengoli, Colin Prentice, Yunke Peng, Shengchao Qiao, Yifan Su, Han Wang, Runxi Wang, Yuhui Wu, Shuxia Zhu and Wei Zheng) and made this paper possible.