Missions

Tropical forests cover approximately 31% of the world's forested area and hold nearly 55% of global aboveground carbon stocks, contributing to about half of the annual forest carbon sink. As such, they play a vital role in climate change mitigation, and accurately characterizing and monitoring them is of paramount importance.
Traditional methods for estimating tree aboveground biomass—and thus aboveground carbon stocks—rely on allometric equations derived from destructive sampling. These equations link easily measurable tree-level variables (e.g., diameter at breast height, total height) to biomass. In the absence of species-specific data, a single generic equation is often applied across diverse species, overlooking the substantial architectural variability observed in these hyper-diverse ecosystems.
Structural traits (e.g., crown dimensions, branch topology and geometry) reflect differences in biomass allocation (e.g., trunk vs. branches) and species-specific growth rules. These traits are strongly influenced by species identity and developmental stage, but also constrained by environmental factors such as competition, climate, and soil conditions.
The successful candidate's role will be to characterise and improve our understanding of these structural variations in natural forests, which is essential for enhancing allometric models and improving estimates and predictions of tropical forest carbon stocks.

Activités

As part of the One Forest Vision (OFVi) initiative [https://www.oneforestvision.org/] and in connection with the Geo-Trees network [https://geo-trees.org/], we are offering a two-year postdoctoral position to contribute to this research topic.
For several years, UMR AMAP and its partners have been collecting terrestrial LiDAR data from monitored and inventoried tropical forest sites, primarily in the Congo Basin (Cameroon, DRC) and Amazonia (French Guiana), covering a range of forest structures and climatic conditions. Thousands of trees, spanning diverse species and functional strategies, have been modeled from this data using Quantitative Structural Models (QSMs) that encode the topology and geometry of entire tree architectures.
The successful candidate will be responsible for:
1. Analyzing existing QSMs to build a structured database of architectural traits and biomass distribution indices at the tree level (notably using and improving the R package aRchi).
2. Expanding this database with new, yet-to-be-processed terrestrial LiDAR data, with support from a team of assistants and a validated processing pipeline.
3. Conducting meta-analyses of architectural traits to assess their variability (within and across species) and explore the effects of environmental gradients and stand structure.
4. Publishing findings in peer-reviewed scientific journals.
These analyses will enhance our understanding of the drivers of architectural diversity in tropical trees and integrate this variability into allometric models to improve large-scale biomass and carbon stock estimation.
Additional datasets (e.g., airborne LiDAR and RGB imagery) are available at some sites, offering opportunities to explore stand-level structure or dynamic processes (e.g., monthly drone monitoring, repeated terrestrial LiDAR acquisitions, dendrometer-based growth monitoring). Depending on the candidate's interests, the project can also explore links between architectural traits and classical functional traits (e.g., leaf, wood, hydraulic traits), for trees already sampled at some study sites.

Compétences

• PhD in forest ecology, tree biology, or related disciplines.
• Knowledge of tropical ecosystems and their conservation challenges is an asset.
• Strong skills in data analysis, statistics, and programming (R, Python) are required.
• Experience with LiDAR data processing and QSM analysis is highly desirable.
• Ability to work independently, collaborate in a team environment, and publish in international peer-reviewed journals.

The application should include:
• A detailed CV, including a full list of publications
• A cover letter
• Contact details for three referees, indicating the nature of your professional relationship with each (recommendation letters also accepted)
• A research proposal aligned with the project themes and available data

Contexte de travail

The postdoc will be based at UMR AMAP (Botany and Modelling of Plant Architecture and Vegetation), an interdisciplinary research unit specializing in tropical vegetation. The candidate will interact with researchers in tropical forest ecology, tree architecture, vegetation modeling, and remote sensing. National and international collaborations will be possible, particularly with teams involved in OFVi and Geo-Trees. A field mission in Cameroon (in collaboration with ENS Yaoundé) is expected for local coordination and additional data collection.