Field-based monitoring of grassland restoration and comparison with long-term ecological (LTER) legacy data

Ecological restoration is a key pillar of the EU Nature Restoration Regulation, yet robust, outcome-oriented evaluation of restoration success remains a major challenge. Grassland restoration monitoring is often limited to short-term, project-based field surveys, while long-term ecological research (LTER) infrastructures provide valuable but underused reference information. This PhD project focuses on developing and testing field-based indicators of grassland restoration success and evaluating how legacy LTER datasets can be used as benchmarks for restoration assessment. The research will be embedded in a national-scale project on standardized monitoring of Pannonian grasslands in Hungary, with strong links to the Kiskun LTER network and ongoing restoration initiatives in protected areas.

The PhD candidate will conduct standardized vegetation monitoring at restored grassland sites representing different restoration pathways (e.g. abandoned arable fields, shrub-encroached grasslands). Fieldwork will focus on structural, functional, and compositional indicators, including vegetation cover, species composition, functional proxies, and indicators of degradation or recovery. A central methodological component is the harmonization and comparative analysis of newly collected field data with long-term and landscape-scale legacy datasets from the LTER network. The candidate will apply statistical analyses to compare restoration trajectories against reference ranges derived from historical and long-term data, and evaluate the strengths and limitations of different reference concepts.

The ideal candidate has a background in ecology, biology, environmental sciences, or a related field, with strong interest in vegetation ecology and ecological restoration. Experience in botanical fieldwork, plant identification, and ecological data collection is essential. Basic skills in statistics and data analysis (e.g. R or similar) are expected, while experience with long-term datasets or monitoring frameworks is an advantage. The PhD is expected to result in at least two D1 publications focusing on (i) the use of legacy data as reference benchmarks in restoration monitoring, and (ii) an integrated assessment of grassland restoration outcomes. 

Contact information: halassy.melinda@ecolres.hu