Carbon leakage in forest ecosystems – towards understanding the real contribution of forests to mitigate climate change

REFERENCE NUMBER

Please use the following reference number: SCEBE-22SF-CLIFE-Ollauri

Either select from the drop down list or refer to the reference number in the prospective applicant text box if applying that way.

BACKGROUND

The ability of forest ecosystems to store carbon (C; e.g. CO2) above and belowground makes afforestation one of the leading measures to fight climate change. Atmospheric CO2 is trapped in the vegetation and then stored in the forest soil, from where it cycles back to the atmosphere as a result of soil respiration (SR). SR is a complex soil microbial process depending on the soil properties and composition, hydro-climatic conditions, and vegetation type. The succession of drying and wetting hydrological cycles within the forest soil seems to trigger “hot-spots” and “hot-moments” of SR, by which large amounts of CO2 are released from the soil. Over 50 % of this CO2 may be released in dissolved form, entering the soil solution and, thus, being potentially lost through soil percolation and then transported to other environmental compartments. Yet, the latter component of the C cycle has been largely neglected, leading to systematic underestimations of SR and to an inaccurate account on the contribution of forest ecosystems to mitigating climate change. 

AIMS

The aims of the project are to:

• Explore the combination of biological, edaphological, and climatic factors regulating Soil Respiration 

• Investigate plant traits contributing to the formation of “hot-spots” and “hot-moments” of soil respiration 

• Quantify C leakage through soil percolation 

• Build numerical models able to describe and predict C leakage in forest soils under different climate scenarios

• Disseminate the project outcomes to relevant stakeholders

CANDIDATE SPECIFICATIONS

The successful applicant will be able to demonstrate understanding of ecosystems functioning and biogeochemical and hydrological cycles. Expertise undertaking field and laboratory work is desirable. A good grasp of statistical methods and tools, such as R, is preferable. Effective oral and written communication skills are mandatory. 

Candidates are invited to submit a more detailed research proposal (of a maximum of 2000 words) on the project area as part of their application.  

HOW TO APPLY

This project is available as a 3 years full-time PhD study programme with expected start date of 1 October 2023.

Candidates are encouraged to contact the research supervisors for the project before applying.

Please note that emails to the supervisory team or enquires submitted via this project advert do not constitute formal applications; applicants should apply using the Application Process page.

Applicants shortlisted for the PhD project will be contacted for an interview within six weeks from the closing date. 

Please send general enquires regarding your application to: [Email Address Removed]