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By Marlene Rimmert

As part of my Master’s degree in Environmental Systems Science at the University of Osnabrück, I did a so-called Master’s project in the POLISES group during my last semester. The aim of such a project is to realize a small own research project and to write a report about it afterwards. I was very pleased that I had the opportunity to carry out my project remotely in addition to my other courses at the university and still get to know the workflows at a research institute like the UFZ.

My Master's project was embedded in the BESTMAP project, which aims to analyse the impact of different policy scenarios on the implementation of agri-environmental schemes (AES) and the resulting environmental impacts. Within the project, an agent-based model was developed to explicitly represent individual farmer decision-making under different policy scenarios and their impacts on different spatial allocations of fields under AES.

In my project, I mainly focused on the analysis of these spatial allocations of fields. Spatially connected fields on which AES are applied can provide more qualitative habitat in the fragmented agricultural landscape as they facilitate environmental processes like dispersal or migration, and thus can be highly relevant for enhancing biodiversity.

The goal of my project was to find possible indicators for measuring the spatial connectivity of field under AES. My work consisted largely of a literature review. I referred to literature from spatial statistics as well as landscape ecology. As a result, I identified two groups of metrics to assess the spatial connectivity of fields: distance-based and graph-based metrics. Distance-based metrics included statistics of spatial clustering such as the nearest neighbour distances or Moran’s I. If the value indicates high clustering, fields under AES tend to be located close to each other and the connectivity of the fields can be assumed to be high. Graph-based metrics are based on graph and network theory, where the landscape is transformed into a mathematical network and connectivity between two fields is determined by a certain distance threshold. In this way, graph-theoretical characteristics of networks can be calculated, which, interpreted together, allow a comprehensive analysis of the connectivity of the landscape.

Despite the literature-based work, I had the opportunity to work with the model code in NetLogo as well as with the data analysis in R. I have learned to familiarise myself with existing extensive code and to carry out my own analyses by trying out first approaches to implement the indicators I have found. As I was always supported and received great feedback to my work, I was able to learn a lot not only about coding and data analysis, but also in terms of scientific writing.

Besides that, I had the opportunity to gain insights into the work of the POLISES group. I had the possibility to attend the weekly working group meetings and visited the institute in Leipzig for a day to see the working life on site.

Overall, the Master's project in the POLISES group was a great opportunity to work on a research project within a larger international research project and in this way gain experience with modelling practices and socio-environmental research.