Postdoc : Impact of heat and drought stress on forest leaf phenology

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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: 

Leaf phenology is the study of the drivers and dynamics of leaf budbreak, unfolding, maturation, and senescence. It is a major determinant of carbon, water, and energy exchanges between forests and the atmosphere. By directly influencing many key biological and biophysical processes, the timing of leaf presence and absence impacts biomass production, local climate, community structure, and forest resilience. However, global changes and associated extreme weather events are disrupting these processes, leading to anomalies such as an increase in late spring frosts, summer leaf loss due to drought, and the development of new leaves in the middle of winter.

Current approaches are generally based on an empirical relationship between observed leaf phenology and standardized air temperature (the macroclimate). This does not capture the actual conditions experienced by trees (the microclimate), nor the interactions between leaf dynamics and underlying ecophysiological processes (e.g., photosynthesis, transpiration). This results in poor models unable to predict the phenological stages of leaf emergence and senescence in future climate. In addition, events such as stress induced defoliation or secondary bud break, which are more frequent, are never captured by these models. This leads to a bias in the interpretation of leaf dynamics as well as in trees’ response to global warming. Vegetation models, such as those used to study the carbon cycle and forest resilience, are thus subject to significant uncertainties.

The goal of this project is to address these gaps by studying the interactions between microclimatic temperature, leaf activity (photosynthesis, transpiration), and the phenological dynamics of the leaves of European temperate tree species at different scales (leaf, individual, ecosystem). By combining experiments, in situ measurements and observations, surface temperature imagery data (thermal camera network, https://leafpace-erc.com/index.php/data/, satellite remote sensing), and process modeling, this project will 1) quantify the role of leaf temperature in controlling leaf growth and senescence and 2) quantify how thermal and water stresses influence these processes.

Research Topic:

Forest dynamics in the context of climate change; responses in phenology and the dynamics of carbon and water cycles under water and heat stress

Research Field:

Environmental physics, forest macroecology, functional ecology, ecophysiology

Objectives:

The overall objective of this project is to study the relationships between leaf temperature, leaf activity, and the dynamics of leaf growth and senescence in forests.

Method:

To achieve these objectives, you will be required to develop a multidisciplinary approach combining field observations, data analysis, and process modeling through three tasks:

1) Analyze the relationships between leaf temperature, leaf activity, and phenology in relation to environmental conditions. For this task, you will combine and analyze multiple datasets acquired at different spatial and temporal scales, such as: i) photosynthesis and temperature measurements at the leaf scale, ii) temperature observations from thermal imaging (camera, radiometric thermometer), phenology (field observations, Phenocam), and carbon, energy, and water fluxes (eddy covariance, sap flow) at the individual and ecosystem scales; and iii) surface temperature and phenology observations from remote sensing (drones, satellites) at the ecosystem and global scales. You might also be required to conduct additional field measurements as well as ad hoc experiments to test certain hypotheses.

2) Develop a phenological model of leaf growth and/or senescence that takes into account leaf temperature and physiological activity. This task will involve evaluating existing models and developing a new phenological model based on the results of Task 1.

3) Specifically study the links between leaf dynamics and water and heat stress. This task will focus particularly on stress-induced senescence and the subsequent restart of leaf growth (second flush) and leaf activity.

References :

Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nature communications 10, 5388; 10.1038/s41467-019-13365-1 (2019).

Peaucelle, M., Peñuelas, J. & Verbeeck, H. Accurate phenology analyses require bud traits and energy budgets. Nature plants; 10.1038/s41477-022-01209-8 (2022).

Peaucelle, M. et al. Representing explicit budburst and senescence processes for evergreen conifers in global models. Agricultural and Forest Meteorology 266-267, 97–108; 10.1016/j.agrformet.2018.12.008 (2019)

Kloos, S. et al. The linkage between functional traits and drone-derived phenology of 74 Northern Hemisphere tree species. Sci. Total Environ. 952, 175753 (2024).

Hänninen, H. & Tanino, K. Tree seasonality in a warming climate. Trends in plant science 16, 412–416; 10.1016/j.tplants.2011.05.001 (2011)

Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year's flushing date in two temperate tree species. Proceedings of the National Academy of Sciences of the United States of America 111, 7355–7360; 10.1073/pnas.1321727111 (2014).

Xie, Y., Wang, X. & Silander, J. A. Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts. Proceedings of the National Academy of Sciences of the United States of America 112, 13585–13590; 10.1073/pnas.1509991112 (2015).

Marchin, R., Zeng, H. & Hoffmann, W. Drought-deciduous behavior reduces nutrient losses from temperate deciduous trees under severe drought. Oecologia 163, 845–854; 10.1007/s00442-010-1614-4 (2010).

Cole, E. F. & Sheldon, B. C. The shifting phenological landscape: Within- and between-species variation in leaf emergence in a mixed-deciduous woodland. Ecology and evolution 7, 1135–1147; 10.1002/ece3.2718 (2017).

Zhao, H. et al. Diverging models introduce large uncertainty in future climate warming impact on spring phenology of temperate deciduous trees. The Science of the total environment 757, 143903; 10.1016/j.scitotenv.2020.143903 (2021).

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

Caldararu, S., Purves, D. W. & Palmer, P. I. Phenology as a strategy for carbon optimality: a global model. Biogeosciences 11, 763–778; 10.5194/bg-11-763-2014 (2014).

Training and skills

• Recommended training: PhD in ecosystem modelling, plant ecophysiology, data analysis, image analysis
• 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, biophysical processes (energy balance, photosynthesis, phenology). Comfort with heterogenous observational datasets and with model-data evaluation. Experience with field work is an advantage.
• 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