Exploration of plant traits –My first year of PhD at LPICEA

Though I just finished my first year of PhD, I’ve done some trait analysis with Dr. Wang for two years. Now I reflected on my past two years, it seems I have gained some knowledge of plant traits but with more unknown things to explore in the future.

From photosynthetic to hydraulic traits, I’ve got the hang of research a bit and expanded my study from application to development of P model (Wang et al., 2017). At first, I had no idea how to combine my interest ‘plant water transport’ with our lab expertise and what I should do for my PhD program. Fortunately, Dr. Wang pointed me a project on leaf traits given my previous scientific experience, and helped me with underlying mechanisms and even code from the scratch. As I learned more about plant traits, I got the feeling of doing research and realised that I’m passionate about ‘plant hydraulics’ which can also implement our lab research.

• P-model based leaf traits prediction

Though P model was first developed to predict GPP, it has potential to tackle other problems in a new way. My first project focused on the application of P model to predict four important leaf traits around Gongga Mountain (Fig. 1) and interpreting the underlying mechanisms. The success of traits predictions (Fig. 2) confirms that functional traits, that are commonly assumed in ecosystem models, to be constant within plant functional types are strongly controlled by – and predictable from – environmental factors, mainly temperature, moisture and light availability, but also elevation acting through atmospheric pressure. Our study provides a more fundamental way to represent trait parameters in ecosystem models.  Another indication is that traits controlled by quick physiological processes like photosynthesis adapt to short-term environment conditions, while traits less plastic adapt to long-term condition.

Fig. 1. The study area. (a) the location of the Gongga Mountain region in China, (b) spatial distributions of the sampled sites in the Gongga mountain region, shown by red dots, (c) the daytime temperature in July (T_dJ) and the ratio of annul actual evapotranspiration to annual potential evapotranspiration (α_p) at the sampled sites. The background to plots (a) and (b) shows elevation.

Fig. 2 The comparison of observation vs. prediction. The traits are leaf mass per area (M_a), leaf nitrogen content per unit area (N_area); the maximum capacity of carboxylation standardized to 25 ˚C (V_cmax25) and the ratio of leaf-internal to ambient CO₂ partial pressure (χ). Observations are site-mean values.

• P-model development: the coordination between hydraulic and photosynthetic traits

When my first project was carried out, we noticed that the large uncertainty incurred by parameter ‘β’ should be the key to bring hydraulic traits into current framework to improve the predictability and expand the theory of P model. So we put some hydraulic traits into our measurement list during the second fieldtrip at Gongga Mountain (Fig. 3). This project explored variation of β and how hydraulic and photosynthetic processes are coordinated, which has long been observed but lack theoretical framework (Brodribb et al., 2002). Based on theory proposed by Prentice et al. (2014), I tried to figure out traits coordination and predict key trait ‘Huber value’ (v_H, the ratio of sapwood to leaf area) that links hydraulic and photosynthetic traits (Fig. 4). Our preliminary results confirm that hydraulic traits are not only coordinated with photosynthetic traits, but influenced by vapour pressure deficit, temperature and elevation. But the prediction of v_H still needs more work (Fig. 4).

Fig. 3 The measurement of hydraulic conductance.

Fig. 4 The comparison of observed and predicted v_H. The prediction is the linear function of V_cmax25 (the maximum capacity of carboxylation), K_s (sapwood-specific hydraulic conductance), χ (the ratio of leaf-internal to ambient CO₂ partial pressure), π_tlp (leaf potential at turgor loss point), c_a (the ambient partial pressure of CO₂), m_c (=(c_i – Γ*)/(c_i + K), c_i­ is the leaf-internal partial pressure of CO₂, Γ* is the photorespiratory compensation point, K is the effective Michaelis-Menten coefficient of Rubisco).

Some people may think it difficult and tiring to carry out the analysis and write a paper, whereas, I was lucky enough to have such a great team including two faraway scientists Colin and Sandy to work with. Science is just like everything else, we have our ups and downs for sure but I think I’m now prepared for that.

Brodribb, T. J., Holbrook, N. M., and Gutiérrez, M. V.: Hydraulic and photosynthetic coordination in seasonally dry tropical forest trees, Plant, Cell & Environment, 25, 1435-1444, 2002.

Prentice, I. C., Dong, N., Gleason, S. M., Maire, V., and Wright, I. J.: Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology, Ecology Letters, 17, 82-91, 10.1111/ele.12211, 2014.

Wang, H., Prentice, I. C., Keenan, T. F., Davis, T. W., Wright, I. J., Cornwell, W. K., Evans, B. J., and Peng, C.: Towards a universal model for carbon dioxide uptake by plants, Nature Plants, 3, 734-741, 10.1038/s41477-017-0006-8, 2017.