SPUN is helping map patterns of mycorrhizal biodiversity, identifying under-sampled areas, and advocating for better protection of these communities.
Human activities and climate change are threatening mycorrhizal fungi at a rate never before seen, but we lack the tools to locate where these threats are happening. To address this, we are using geo-spatial data to identify and quantify threats to underground biodiversity.
Biodiversity mapping uses data to make predictions about the biodiversity across all of Earth’s ecoregions. With this kind of machine learning work there is always a degree of uncertainty. SPUN sees areas with high uncertainty as opportunities to “ground truth” our predictions — collecting real-life samples from areas where our model has made predictions allows us to check those predictions against the biodiversity we find and strengthens our models.
How does SPUN choose locations for sampling expeditions? To produce the most valuable data, we make strategic decisions when we choose where to sample for mycorrhizal fungi. We prioritize regions that have unique characteristics and low sampling intensity — this way we make sure to collect samples in areas that will have the highest impact on our effort to map global mycorrhizal biodiversity.
Mycorrhizal fungi are tiny organisms that live in the soil and form partnerships with plant roots, helping plants get nutrients. They play a big role in keeping Earth’s ecosystems healthy, but we don’t fully understand where different types of these fungi are found around the world. This knowledge gap makes it hard to track and protect these important underground networks.