Postdoc position: Development of a leaf phenology model based on ecophysiological optimality assumptions

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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, the study of the drivers and dynamics of leaf budburst, unfolding, maturation and senescence, is a key process of carbon, water and energy exchanges between forests and the atmosphere. Current approaches to modeling leaf unfolding and senescence rely mainly on the empirical link between leaf phenology and standardized air temperature. This simplified representation of leaf phenology does not allow us to correctly simulate the spatial and temporal variability of forest dynamics, nor to represent the impact of extreme events such as late frosts, droughts and heat waves. This is therefore one of the most significant sources of uncertainty in terrestrial biosphere models (TBMs), resulting in significant biases in the representation of annual forest productivity and associated biogeochemical cycles (carbon, water, etc.).

In order to overcome this empirical and inadequate representation of phenological processes in TBMs, this postdoc project aims to explore new approaches based on optimality principles to simulate and predict leaf dynamics from a priori information on a species' strategy and local environmental conditions.

This 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 (several national and international teams).

Context:

Leaf phenology, i.e. leaf development, growth and senescence, has a direct influence on forest productivity and biomass. It also influences local weather conditions and long-term climate through transpiration, albedo and carbon storage (Peñuelas et al. 2009). The rapidity of current climate change is translating into major disruptions in phenological cycles on a global scale, increasing the risks (e.g. of frost or spring drought) faced by trees that were adapted to past environmental conditions (Peaucelle et al. 2019). Despite centuries of research and observations, a thorough fundamental understanding of the environmental drivers governing leaf phenology is still lacking. The misrepresentation of phenology in terrestrial biosphere models is considered one of the main uncertainties in carbon cycle estimates and future climate predictions. Temperature is recognized as the main driver of leaf phenology in extra-tropical ecosystems. For this reason, phenological models generally rely on metrics based on the air temperature preceding phenological events, often in the form of a sum of degree-days.

Numerous alternative models have been proposed to describe budburst, growth and leaf senescence, including sunshine duration, or photoperiod, as another climatic variable, but with often limited success compared to a model based solely on a sum of degree-days. One of the reasons for the limitations of empirical models based on temperature alone is that today's climate is evolving too fast, so that models based on past, relatively “stable” climatic conditions are unable to accurately predict phenology in recent years, which have seen a number of record-breaking climatic extremes. Another reason is quite simply that the ecophysiological processes governing leaf growth and fall are poorly understood, and cannot be correctly simulated using only environmental conditions as the main input.

To address these limitations, this project aims to explore new mechanisms based on the optimality of ecophysiological processes, in order to better understand and simulate leaf dynamics in forests. Leaf phenology is closely linked to leaf physiology. There is a trade-off between leaf activity and structure, known as the leaf economic spectrum (Wright et al, 2004). This spectrum reflects species strategies and trade-offs between plant functions; for example, between leaf longevity and photosynthetic capacity, or leaf thickness and resistance to water stress. Species optimize available resources (e.g. nutrients, light, etc.) by investing in leaf metabolism and structure in different ways. The different strategies are reflected in a range of functional traits along a gradient characterizing “fast” and “slow” species (Reich et al. 2014; e.g. deciduous vs. coniferous). Theories of eco-evolutionary optimality (Franklin et al. 2020; i.e., principles that constrain plant and ecosystem behavior through natural selection and self-organization) are promising avenues to explore to explain plant function and acclimation to local conditions, providing simple, unified and parsimonious physiological explanations. Indeed, natural selection rapidly eliminates traits and strategies that are neither efficient nor competitive. Such assumptions can be used to predict gradients, particularly in regions where observations are scarce. Only a few attempts have been made to use optimality approaches to explore leaf area dynamics in forests, and these have been limited to maximizing carbon gain or water use.

While classical studies and models attempt to explain phenological cycles as discrete events triggered by environmental conditions, here we will consider leaf phenology as the integrated result of plant activity throughout the year, depending on environmental conditions and species strategy.

Objectives:

During this project the candidate will have to develop a new model according to which trees produce leaves when it is beneficial to them and lose them when the physiological cost (e.g. leaf maintenance, hydraulics or nutrient-seeking system) is too high. One of the main objectives will be to target the key processes linked to the costs and benefits for a tree of having leaves, in order to define a new model based on multi-criteria optimization, enabling phenological processes to be simulated in a dynamic and mechanistic way.

References:

Peñuelas, J., Rutishauser, T. & Filella, I. Ecology. Phenology feedbacks on climate change. Science (New York, N.Y.) 324, 887–888; 10.1126/science.1173004 (2009).

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).

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

Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. Journal of Ecology 102, 275–301; 10.1111/1365-2745.12211 (2014).

Franklin, O. et al. Organizing principles for vegetation dynamics. Nature plants 6, 444–453; 10.1038/s41477-020-0655-x (2020).

Training and skills

• Recommended training: PhD in science or equivalent
• Knowledge required: Strong programming skills (R, Python, Fortran...), data analysis and modeling, knowledge of plant ecophysiology and functional ecology.
• Appreciated experience: Statistical analysis, theoretical research on the principles of optimality and multicriteria optimization.
• 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 our teams, you benefit from (depending on the type of contract and its duration):

- up to 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.

How to apply

I send my CV and my motivation letter