This work will explore the relationship between biodiversity and ecosystem function at different scales by combining satellite data (primarily Landsat and Sentinel-2) with in situ data and models. It will utilize several remote sensing-based Essential Biodiversity Variables (EBVs), specifically, the Ecosystem Structure EBV “Ecosystem Distribution” (from which habitat fragmentation and connectivity can be derived), and the Ecosystem Function EBV “Primary Productivity”, both of which are based on remotely sensed data. Understanding this relationship is essential for several SDGs and is critical for ecosystem management and accurate forecasting models, but also advances the application of satellite data for monitoring and managing ecosystem function and the services ecosystems provide to humans. The result of this study, which will be led by the Quebec Centre for Biodiversity Science in close collaboration with GEO BON’s Working Groups and Biodiversity Observation Networks, will be captured in a peer-reviewed open-access publication and in freely available software, workflows and methodology.
This reflects some of the early activity steps that will be built upon as the work progresses:
Background: Theory and models predict the strength of the relationship between biodiversity and ecosystem functioning (BEF) to vary with spatial scale. In this study, we are testing this understanding by assessing how changes in tree diversity mediate primary production at different spatial grains. We are applying spectral variation methods to MODIS remote sensing data to estimate tree diversity (alpha and beta) at these different spatial grains. We corroborate these estimates of diversity with in situ measures of tree diversity taken from thousands of permanent plots distributed across southern Quebec. Next, we quantify the slope of the correlation (regression estimate) between these spectral diversity estimates (Rao’s Q and Shannon indices) with measures of primary production (NDVI and EVI) at those scales. We obtain different slope estimates at different spatial grains. We then assess the nonlinear functional form describing how BEF slope values change across the different scales and explain this as turnover in the distribution of plant diversity and primary production across geographic gradients in topography, soils and climate. We expect that remote sensing derived estimates of spectral variation will be a powerful way to quickly assess how biodiversity change at different scales may alter ecosystem production across different ecoregions around the world.