PhD project offered by the IMPRS-gBGC in July 2024

Studying Impacts of Nutrient on Biodiversity-Ecosystem Function Relationships Using Multiple Data Streams in Semi-Arid Regions

Sung-Ching Lee, Jana Wäldchen , Javier Pacheco Labrador , Christine Römermann , Markus Reichstein

Project description

Savanna-type ecosystems in semi-arid regions typically consist of scattered trees and a coexisting grass layer. They play a major role for the inter-annual variability of the terrestrial carbon sink as they are sensitive to fluctuations in water availability. Nitrogen (N) and phosphorus (P) are crucial nutrient influencing ecosystem functions, plant traits, and water use efficiency. In many terrestrial ecosystems, anthropogenic N deposition is expected to lead to disruptions in the stoichiometric balance between plant-accessible N and P. Responses of ecosystem carbon/water cycle to nutrient availability and N:P imbalances are nonlinear and complex. Based on the data collected at the Majadas de Tiétar research stations, we know the fertilized areas have higher variability in carbon fluxes and the N-added area uses more water. This may decrease resilience to heat and drought extremes, especially soil drought, compared to the area with more balanced N:P. However, we can not understand the underlying mechanisms due to the lack of vegetation composition monitoring.
With the advancement in methodologies that bridge the fields of computer science and botany, we have an opportunity to better understand how N:P ratios affect vegetation composition and hence ecosystem-atmosphere fluxes. Specifically, the PhD will:

Conduct vegetation surveys using Flora Incognita and Flora Capture App to improve site specific automated species identification.
Install field cameras at the research sites and test automated recognition of vegetation composition changes in collaboration with the Flora Incognita initiative.
Evaluate the use of imagery beyond the visible spectrum (e.g. multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence) for enriching information on vegetation functions.
Combine eddy covariance data to assess biogeochemical fluxes, water use efficiency, and carbon-water decoupling of herbaceous layer under different N:P levels.

Working group & collaboration

The candidate will work in the Eco-Meteorology and Biod.AI groups in the Department of Biogeochemical Integration at the Max Planck Institute for Biogeochemistry, and the Biodiversity of Plants groups at the Friedrich Schiller University in Jena. Furthermore, there will be a close collaboration with the Data-Intensive Systems and Visualisation group at TU Ilmenau. The candidate will have chances to work closely with collaborators in Spain (e.g., SpecLab-CSIC) and Italy. (https://www.bgc-jena.mpg.de/majadas)

Requirements for the PhD project are

Applications are open to well motivated and independent students from any country who:

Hold a Master’s degree in ecology, geography, botany, plant science, or related;
Are experienced in eddy covariance and/or machine learning;
Had programming experience in a modern computing language (e.g., Python, R);
Have excellent written and oral English language skills, be willing to work in a team of scientists.

The Max Planck Society (MPS) strives for gender equality and diversity. The MPS aims to increase the proportion of women in areas where they are underrepresented. Women are therefore explicitly encouraged to apply. We welcome applications from all fields. The Max Planck Society has set itself the goal of employing more severely disabled people. Applications from severely disabled persons are expressly encouraged.

Left to right: Landscape of the Majadas de Tiétar research station; Eddy covariance flux tower; Automated identification of plant species via Flora Incognita; Hyperspectral measurements at Majadas de Tiétar.