Ongoing Projects
Here you find a selection of ongoing research projects at the Biodiversity Data Lab.
BIOSCANN - BIOdiversity Segmentation and Classification with Artificial Neural Networks
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.
View the interactive data product here.
You can find the project's GitHub page here.
News: Now the preprint is available here.
Modeling of seasonal changes in arthropod diversity - combining metabarcoding with deep learning
In this project we present a CNN biodiversity modeling approach which can be applied to predict the expected species richness over time and space of any organism group, given a time-series of biodiversity inventories. Here we utilized a metabarcoding biodiversity inventory focused on arthropods combined with 25 environmental features to predict the expected number of arthropod species at any given location and time of the year in Sweden.
You can access the preprint here and the GitHub page for the project here.
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High-resolution insect diversity predictions from DNA-based monitoring
We predict the expected insect biodiversity across Sweden at 10-m resolution using models trained on DNA data gathered from Malaise-traps (203 traps, 100 sites). The models integrate these biodiversity data with high-resolution environmental layers such as land cover, vegetation structure, hydrology, and climate. Presences and absences for each of the 11,000 insect species in our dataset were modeled in a joint species distribution model (jSDM) that estimates species-level occupancy while sharing information across taxa, improving inference for rare and sparsely detected species. We produced nationwide richness maps as well as species-specific probability maps (species distribution maps) at scales relevant for local management decisions. This jSDM-eDNA framework delivers scalable, and standardized biodiversity monitoring at management-relevant spatial resolution.