From sentinels to avatars? Scaling from ecological data to ecosystem change
The National Ecological Observatory Network (NEON), a project funded by the National Science Foundation, will collect consistent, calibrated data at sites distributed throughout the United States for 30 years. The goal of NEON is to enable understanding and forecasting of the impacts of climate change, land use change, and invasive species on continental-scale ecology. NEON is planning uniform protocols on over 500 physical and biological variables at 3 sites in each of 20 domains, a design that allows assessment of concomitant trends (sentinels) at the scale of the entire U.S. Many of the variables that will be collected by NEON are potentially scalable to the domain level (avatars). We investigated the science basis for scale up from trends (sentinels) to status and trends (avatars) over some 220 biological variables, some of which consist of hundreds of component measurements (e.g. species diversity).
The spatial scale-up from fully censused area in any one year, to a site, ranged from 50 km2)/(0.4X10-6 km-2) = 1.25X107 (beetles) to (50 km2)/(0.5 km2) = 102 (birds). The spatial scale-up to 20 domains from 60 sites, by area, is (9.8X106 km2)/(50 km2) = 2X105. In a catalog of 31 high level data products for biological variables 22 are currently listed as sentinels, with limited scale-up in the remaining variables. We identified several modes of scale-up: (1) samples weighted by habitat coverage; (2) interpolation to external networks such as AmeriFlux, eBird, and the Forest Inventory Analysis; (3) process-based functional relations to driver (primarily physical) variables. Sentinel variables at the scale of the entire US allow experimental studies to be put in regional or larger contexts, allow investigators to identify patterns of covariance between biological variables and drivers at multiple scales, allow investigators to propose measurements at NEON sites that could move a variable from sentinel to process based avatar, and permit rapid advances in the development of scaling methods in ecology.