A postdoctoral position is available in the AMAP lab located in Montpellier, France (https://amap.cirad.fr/en/).
What we would like to achieve and why this is important:
Covering just 7% of the Earth’s land surface, tropical forests play a disproportionate role in the biosphere: they store about 25% of the terrestrial carbon and contribute to over a third of the global terrestrial productivity. They also recycle about a third of the precipitations through evapotranspiration, thus contributing to generate and maintain a humid climate regionally, with positive effects extending well beyond the tropics. However, the seasonal variability in fluxes between tropical rainforests and atmosphere is still poorly understood. As an illustration, dynamic global vegetation models (DGVMs) typically simulate a decrease in productivity with a decline in precipitation and soil water availability (Restrepo-Coupe et al. 2017), while observations in light‐limited rainforests point to a dry‐season increase in gross primary productivity (Aguilos et al. 2019). Better understanding the processes underlying flux seasonality in tropical forests is thus critical to improve our predictive ability on global biogeochemical cycles.
A few previous studies suggested the importance of the variation of leaf properties with leaf age to explain fluxes seasonality (Wu et al. 2016, 2017; Albert et al. 2018; Menezes et al. 2022), but data to support this is limited to a few sites and very few tree species and individuals, at odds with the considerable taxonomic, functional and phenological diversity in these forests. We have gathered an unprecedented dataset of trait and spectral measurements on leaves of various leaf ages from canopy trees in both Paracou, French Guiana, and M’Balmayo, Cameroun. Leaf ages were determined by combining temporal series of individual crown images acquired through drone flights with in-situ observations of tree development. Traits include classical economic traits (leaf mass per area, nitrogen and phosphorous contents), leaf water potential at turgor loss point (a drought tolerance trait), as well as photosynthetic capacities (Vcmax, Jmax, determined by fitting curves of assimilation response to CO2 concentration, using a LICOR LI-6800) on a subset. Your main objectives will be to (i) characterize the variation of leaf traits with leaf age, (ii) identify the (in)consistencies of such variations across species and sites, (iii) assess the capacity of spectral data to inform and predict such variation, through relevant and robust statistical analyses, (iv) lead the writing of a manuscript. Direct perspectives include (i) the exploration of the anatomical basis of such trait variation with leaf age (using ~250 anatomical sections we have performed on a subset of leaves), (ii) the up-scaling of leaf-level properties to the ecosystem-level (using drone-acquired temporal series of tree and stand level imagery and/or LiDAR data) and comparison with fluxes derived from the eddy-flux tower at Paracou. Your ability to propose and develop your own questioning and analysis method is highly encouraged.
Who you are:
• You have a PhD in ecology or a related field.
• You are at ease and have experienced with R programming and statistical analyses
• You are eager to contribute to a better understanding of tropical forest ecosystem functioning within a multidisciplinary and international team that combines different approaches (remote sensing, ecophysiology, plant architecture, modeling).
• You have excellent written and verbal communication skills
What we offer you:
• A position in a bustling Mediterranean city, south of France, for a period of one year, potentially renewable.
• Funding for conference or publication costs.
• Opportunities for field work in tropical forest sites (French Guiana, Cameroon)
• An inclusive, supportive and collaborative environment where science and human ethics are highly valued
The position is part of the CoForFunc project (https://coforfunc.eu/), funded by Biodiversa+, and the PHENOBS project (https://www.labex-ceba.fr/en/home/strategic-projects/), funded by the Labex CEBA, which gather collaborators from AMAP lab, Montpellier; CREAF, Barcelona; ENEF and the University of Yaoundé, Cameroon; ECOFOG lab, French Guiana; and the University of Cambridge, UK. Among others, you will closely interact with Isabelle Maréchaux (functional ecology and modelling), Nicolas Barbier (remote sensing and tropical forest ecology), Patrick Heuret (plant architecture, anatomy and ecophysiology) and Gilles Dauby (tropical biodiversity, ecology of African tropical forests), as well as Dominique Lamonica (statistical analyses).
Review of applications will continue until the position is filled. To apply, please use one of the following links:
https://euraxess.ec.europa.eu/jobs/282620
https://emploi-recrutement.ird.fr/job/emploi-chercheur-en-ecologie-fonctionnelle-tropicale-et-analyse-de-donnees-h-f_331.aspx?LCID=2057
References:
Aguilos, M., Stahl, C., Burban, B., Hérault, B., Courtois, E., Coste, S., et al. (2019). Interannual and Seasonal Variations in Ecosystem Transpiration and Water Use Efficiency in a Tropical Rainforest. Forests, 10, 14.
Albert, L.P., Wu, J., Prohaska, N., de Camargo, P.B., Huxman, T.E., Tribuzy, E.S., et al. (2018). Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest. New Phytologist, 218.
Menezes, J., Garcia, S., Grandis, A., Nascimento, H., Domingues, T. F., Guedes, A. V., … & Quesada, C. A. (2022). Changes in leaf functional traits with leaf age: when do leaves decrease their photosynthetic capacity in Amazonian trees?. Tree physiology, 42(5), 922-938.
Restrepo-Coupe, N., Levine, N.M., Christoffersen, B.O., Albert, L.P., Wu, J., Costa, M.H., et al. (2017). Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison. Glob Change Biol, 23, 191–208.
Wu, J., Albert, L.P., Lopes, A.P., Restrepo-Coupe, N., Hayek, M., Wiedemann, K.T., et al. (2016). Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests. Science, 351, 972–976.
Wu, J., Serbin, S.P., Xu, X., Albert, L.P., Chen, M., Meng, R., et al. (2017). The phenology of leaf quality and its within-canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests. Global Change Biology, 23, 4814–4827.
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