Postdoc position: Development of an optimality-based leaf senescence model

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

You will be hosted by the Unité Mixte de Recherche Interactions Sol Plante Atmosphère (UMR 1391 ISPA) 

Project description: 

Processes governing autumnal leaf senescence are not well understood, leading to very large uncertainties on the terrestrial carbon sink. Current land surface models predict autumnal leaf senescence based on empirical cold degree day sums or photoperiod thresholds calibrated on phenological records. These models implicitely assume leaf shedding is driven by limited resources (heat or sunlight) but without a clear quantification of this limitation. Consequently they cannot predict senescence advancement caused by drought or heat stress events. The 2018 and 2022 European events produced widespread early browning and defoliation that no global model anticipated.

We argue that optimality theory could help resolve this problem. Leaf shedding can be seen as a decision about an organ that already exists (the leaf) and reports its own state: assimilation, nitrogen content, water potential. Defining a marginal criterion is thus possible. The existing “leaf economics” literature exploited this concept to predict leaf lifespan, but stopped short at predicting phenological phases

This project aims to develop senescence as an economic decision where a leaf cohort is shedded when its expected remaining return falls below what can be salvaged from it plus the risk it removes. The framework treats nitrogen as a salvageable stock, water and heat as hazards, and the timing of shedding as the quantity to be predicted rather than prescribed.

This two years (+1) postdoc project is part of the European ERC Starting LEAFPACE project, in which you will be working in close collaboration with the researchers and PhD students involved in the project and several national and international teams working on vegetation modelling and phenology.

Objectives:

O1 - Formalise senescence as a cohort-resolved marginal decision, combining carbon return, nitrogen salvage value and mortality hazard, with no (few) parameter calibrated on observed phenological dates.

O2 - Derive resorption efficiency as a model output rather than an input, parameterising only the structural nitrogen floor (proficiency).

O3 - Extend the model to drought and heat induced senescence and defoliation, and evaluate it against recent European events.

O4 - Deliver a formulation that can be implemented in a DGVM (e.g. ORCHIDEE), benchmarked against existing empirical senescence models on anomalies, trends and events.

Key References:

Leaf economics and optimal turnover

Kikuzawa, K. (1991). A Cost-Benefit Analysis of Leaf Habit and Leaf Longevity of Trees and Their Geographical Pattern. The American Naturalist, 138(5), 1250–1263.

Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827; 10.1038/nature02403 (2004).

Hikosaka K. Leaf canopy as a dynamic system: ecophysiology and optimality in leaf turnover. Ann Bot. 2005 Feb;95(3):521-33. doi: 10.1093/aob/mci050. Epub 2004 Dec 7. PMID: 15585542; PMCID: PMC4246797.

Nitrogen resorption

Killingbeck, K.T. (1996), Nutrients in Senesced Leaves: Keys to the Search for Potential Resorption and Resorption Proficiency. Ecology, 77: 1716-1727.

Vergutz, L., Manzoni, S., Porporato, A., Novais, R.F. and Jackson, R.B. (2012), Global resorption efficiencies and concentrations of carbon and nutrients in leaves of terrestrial plants. Ecological Monographs, 82: 205-220. 

Estiarte M, Peñuelas J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob Chang Biol. 2015 Mar;21(3):1005-17. doi: 10.1111/gcb.12804. Epub 2015 Jan 8. PMID: 25384459.

Senescence models and the sink-limitation debate

Delpierre, N., Dufrene, E., Soudani, K., Ulrich, E., Cecchini, S., Boé, J., & François, C. (2009). Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agricultural and Forest Meteorology, 149, 938-948.

Zani, D., Crowther, T.W., Mo, L., Renner, S.S., & Zohner, C.M. (2020). Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science, 370, 1066 – 1071.

Lu, X., & Keenan, T. F. (2022). No evidence for a negative effect of growing season photosynthesis on leaf senescence timing. Global Change Biology, 28, 3083–3093. 

Optimality principles 

Stocker, B. D., Wang, H., Smith, N. G., Harrison, S. P., Keenan, T. F., Sandoval, D., Davis, T., and Prentice, I. C.: P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production, Geosci. Model Dev., 13, 1545–1581

Joshi, J., Stocker, B.D., Hofhansl, F. et al. Towards a unified theory of plant photosynthesis and hydraulics. Nat. Plants 8, 1304–1316 (2022).

Zhou, B., Cai, W., Zhu, Z., Wang, H., Harrison, S.P. and Prentice, I.C. (2025), A General Model for the Seasonal to Decadal Dynamics of Leaf Area. Glob Change Biol, 31: e70125. 

 

Training and skills

• Recommended training: PhD in ecosystem modelling, plant ecophysiology
• Knowledge required: Strong scientific programming (R, Python, Fortran for LSM coupling...), data analysis and modeling, knowledge of plant ecophysiology and functional ecology.
• Appreciated experience: Experience with optimality or trait-based vegetation modelling, multicriteria optimization or land surface model development is an advantage. Comfort with heterogenous observational datasets and with model-data evaluation.
• Skills sought: Autonomy, ability to work in a team and collaborate, to write (scientific articles, synthesis) and express oneself (scientific conferences) in English, ability to supervise trainees and PhD students.

INRAE's life quality

By joining INRAE, you can benefit from, depending on the type and duration of your contract:

- up to 30 days of annual leave + up to 15 additional days off (for full-time employees);
- access to the French public healthcare system;
- 50% of the cost of your supplementary health insurance covered by INRAE;
- childcare and parenting support;
- training and career guidance;
- social assistance and financial support;
- holiday vouchers and accommodation at preferential rates;
- sports and cultural activities;
- subsidised staff restaurants.

How to apply

I send my CV and my motivation letter