ClimBEco PhD students

My research lies at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. It focuses on ensuring the credibility and interpretability of machine-learning-based approximations for partial differential equations that integrate data. Climate modelling serves as the primary application domain for testing the developed methods.

My project aims to identify solar storms over the last 12000 years using ice cores. By analyzing the concentration of cosmogenic radionuclides, we can investigate past solar variability.

I am interested in understanding how anthropogenic stressors reshape ecological systems and in developing tools to mitigate biodiversity loss. My PhD research focuses on building quantitative models and machine-learning workflows to predict ecological impacts and support decision-making under multiple, often competing, objectives. I apply these tools in real-world contexts, such as forest management and agriculture practices, to inform strategies that minimize biodiversity loss while accounting for uncertainty.

In my research I am investigating the short-term climate impact of forest management strategies such as clearcutting for an even-aged forest, specifically the effect on greenhouse gas emissions resulting from such interventions. I am using data observed in the field paired with high-spatial resolution drone imagery and machine learning modelling in the research.

My PhD project focuses on estimating lake bathymetry and water-level dynamics using Earth Observation data and modelling approaches. The project aims to improve scalable methods for monitoring freshwater resources and provide insights into lake systems' responses to climate and environmental change, supporting sustainable water management.

My research focuses on quantifying the spatiotemporal characteristics, human exposure, and mortality burden of PM2.5 pollution from landscape fires. Currently, I am integrating high-resolution satellite-driven modelled fire-sourced PM2.5 concentrations with gridded population data to map global pollution patterns and estimate associated human exposure from 2001 to 2023.

My research aims to understand the effects of climate change, specifically drought, on Swedish forests. My PhD project focuses on improving the dynamic vegetation model LPJ-GUESS to better simulate drought stress in Swedish forests. We use data from the Integrated Carbon Observation System (ICOS) to parameterize and validate the simulations. Once the model is complete, it will be possible to predict areas that are vulnerable to future drought and identify forest management strategies that are more resilient to drought events.

My research focuses on exploring the potential of continuous-cover forestry (CCF) to advance sustainable forest management in the context of climate change and biodiversity challenges. Using an interdisciplinary approach, I examine how CCF may support multiple forest functions in southern Sweden. Through this work, I aim to provide an integrated understanding of the conditions and implications of a broader transition toward CCF.

My PhD project investigates how pesticide exposure affects pollinator communities across agricultural landscapes. Using field data, I study the sources and pathways of pesticide exposure for pollinators and explore how pesticide risk influences pollinator diversity, abundance, and community composition. I am also evaluating whether flower plantings can mitigate the negative effects of pesticide exposure on pollinators. By combining ecological field studies and risk assessment approaches, I aim to contribute to the development of improved pesticide risk indicators that better reflect risks to pollinator communities in real-world landscapes.

My research involves landscape-scale multi-taxa biodiversity monitoring, aiming to understand the cost-effectiveness of monitoring different biodiversity indicators in the context of reforestation. My fieldwork for this project is in a mosaic agricultural landscape in Panama, working with a large-scale reforestation project developer.

In my PhD project, I am investigating whether and how wild bees are impacted by changing temperatures, using mason and bumble bees as study species. I am specifically interested in how changes in temperature during development affect the life histories of these bees, and therefore what ecological impacts we can expect given predicted climate scenarios.

My project, The Political Feasibility of Compensation: On the Limits and Possibilities for Environmental Policy, examines when and why compensation mechanisms can reduce resistance to environmental policies addressing climate change and biodiversity loss. The project focuses on public willingness to compensate different types of policy losers and the implications this has for political feasibility, legitimacy, and coalition building.

I aim to understand how tree architecture and biomass vary across elevation gradients and forest disturbance in the Montane Brazilian Atlantic Forest, in order to improve forest carbon estimations. To achieve this, I will combine ground measurements from permanent forest plots with terrestrial laser scanning.

My PhD project explores how livestock grazing influences biodiversity in Swedish semi-natural grasslands, with a focus on plants and pollinators. Using a combination of ecological fieldwork and remote sensing, I investigate grassland conditions and grazing patterns across landscapes. The goal is to support biodiversity-friendly grassland management and improve large-scale environmental monitoring.

My research focuses on the impact of trees to influence the global methane burden. Recent studies have shown that trees could be either sinks or sources of methane to the atmosphere, and to measure this I am developing and deploying a method for tree stem chamber measurements as well as analyzing isotopic fractionation.

My PhD project investigates potential effects of human land-use on wild plant evolution. Since different land-use matrices lead to the assemblage of different pollinator communities, varying selection pressures may be mediated on animal-pollinated plants in different landscapes. Such human-caused evolutionary effects could be important for wild plant conservation and management.

In my research project, I use Bayesian modeling to approximate the production of radiocarbon in the atmosphere. The model incorporates a carbon cycle, geomagnetic field, and solar activity model. The aim of the project is to investigate the influence of climate signals on radiocarbon production rate reconstructions, as well as improving geomagnetic field and solar activity reconstructions.

My research focuses on investigating aerosol surface processes and chemistry using APXPS. By investigating the distinct chemical properties and reaction mechanisms at aerosol surfaces compared to the bulk phase, my work aims to provide a microscopic understanding of atmospheric reactions and their impacts on climate and the environment.