Managing Earth’s life support systems requires assessing risks these systems face and monitoring how systems are changing. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES)—meant to parallel the Intergovernmental Panel on Climate Change (IPCC)—has emerged as a critical effort to assess the risks of global change for biodiversity and ecosystem services, revealing the need for data streams that can be used to monitor trends in biodiversity and ecosystem functions across continents and biomes. The increasing availability of satellite data and launch of new sensors transforms the capabilities for continuous monitoring of biodiversity on Earth. However, meaningfully monitoring requires the development of methods for integrating remotely sensed data and biodiversity information measured on the ground. Together with students and post docs my lab and many collaborators across institutions, I am working to address a suite of use-inspired questions related to the causes and consequences of change in biodiversity, based in part, on my experience working on the IPBES Americas regional assessment.
Specifically, we are harnessing increasingly available sensor technologies to
1) detect changes in plant functional composition and biodiversity and link them to ecosystem function in manipulated plant diversity experiments
2) accurately detect tree disease, specifically oak wilt (Bretziella fagacearum), to enable early treatment capable of containing its spread
3) predict belowground soil and microbial processes from remotely sensed aboveground vegetation chemistry.
Results/Conclusions
We have found that biodiversity treatments in manipulated experiments can be predicted from airborne sensors and used to predict ecosystem functions, such as productivity. We have also made advances in detecting tree disease in Minnesota. Specifically, we have shown that oak wilt symptoms can be remotely sensed, and we are currently studying how the progression of the disease influences physiological function in different oak lineages, enhancing our ability to detect it remotely. We have also demonstrated that remote sensing of aboveground vegetation chemistry, productivity and inputs to soil can predict belowground soil processes. Advances in each of these realms contributes to the broader goal of developing data streams that can be used to monitor biodiversity for large scale risk assessment of changes in biodiversity.