Research
Measuring, modelling, and mapping biodiversity at useful resolution.
Ongoing Projects
BIOSCANN implements a deep-learning segmentation model that integrates multiple layers of environmental data -- satellite images, airborne laser scanning, and soil information -- to identify forests of high biodiversity value in Sweden. With this approach we produce a national data product for Sweden with high-resolution biodiversity predictions.
We predict the expected insect biodiversity across Sweden at 10-m resolution using models trained on DNA data gathered from Malaise traps. The models integrate biodiversity data with high-resolution environmental layers such as land cover, vegetation structure, hydrology, and climate. This jSDM-eDNA framework delivers scalable, standardized biodiversity monitoring at management-relevant spatial resolution.
This project investigates the predictive accuracy of large-scale biodiversity models. Using a global dataset of 25,987 sites from 681 studies, we evaluate linear mixed models and Bayesian hierarchical models for alpha and beta diversity. The models are tested on interpolation and extrapolation capabilities, revealing clear limits to prediction.
A Python package for automated environmental data retrieval in ecological modelling. The service gives users granular control over data synthesis, allowing efficient download of geospatial information from repositories including Google Earth Engine and downstream predictive modelling applications.
Research outputs
Peer-reviewed articles from the lab and its collaborators.
