Swedish Biodiversity In Time and Space SweBITS
To understand complex ecosystems and environmental impact, we need information about the organisms that are present in our environment. We also need to have long time series to predict changes of the biodiversity in the future. Until now it was difficult find the information. Our project will contribute to biodiversity and biosafety research in Sweden and how to prepare for outbreaks of existing and new pests.
Ecosystems are complex and dynamic biological systems that represents a network of interactions between the different organisms and between the organisms and the environment. The purpose of this project is to model the ecosystem in order to understand how biodiversity is influenced by changes in the climate and land use, and then to apply these models to predict future changes in biodiversity, focusing on changes that influences the health of humans, livestock, agriculture, forestry and the ecosystem itself. Modelling complex systems requires high resolution data, but importantly also long time series. We are in this project using a unique archive of air filters that represents an unbroken 50-year line of samples collected weekly at six locations across Sweden.
We have as a proof-of-principle shown that we can extract high quality DNA from these filters and by sequencing identify the organisms. As air sampling captures most plants and fungi (through pollen and spores) as well as algae, bacteria and viruses (including soil and water microbes as they are distributed in aerosols) these samples represents a large part of the total biodiversity.
At the moment we are analysing biodiversity data from Kiruna in the north and Ljungbyhed in the south in order to study how climate change is influencing biodiversity in the Arctic region when compared to the south of Sweden. We will also investigate how the distribution of human and agricultural pathogens have been and is likely to change in the future. Using the sequences we obtain we will also be able to track historical prevalence of antibiotic resistance across Sweden.