The bridge from canopy condition to continental scale biodiversity forecasts, including the rare species of greatest conservation concern

Emerging remote sensing tools have the potential to track biodiversity changes that are happening now are anticipated for coming decades, with new insights on the foundations of terrestrial food webs, particularly high-quality masting fruits, nuts, and seeds that make up 30-100% of herbivore diets. The need for scientific progress on the foundations of terrestrial food webs is most acute for the threatened species of greatest conservation concern. Lidar and hyperspectral imagery provide a high-dimensional representation of canopy structure and its changing spectral reflectance properties over time that are only just beginning to be explored. Emerging insights on canopy condition must be linked to the production of mast that supports herbivores. The Masting Inference and Forecasting (MASTIF) network, coupled with remote sensing and herbivore monitoring at National Ecological Observatory Network (NEON) sites, provides a first opportunity to fully calibrate changes in canopy condition, mast production, and herbivore responses at a continental scale. Building on NASA-AIST support to initiate joint analysis of NEON and remote sensing products, this proposal confronts three main prediction challenges for ecological forecasting, i) translating the most recent remote sensing products into the canopy condition and drought stress metrics having greatest promise for predicting the supply of mast resources to herbivores, ii) leveraging information from the full community of species to improve prediction for the rare species of greatest conservation concern, and iii) resolving the big-data challenge represented by spatio-temporal dependence in large, raster-based arrays of remotely sensed canopy variables and climate. We will create a web visualization portal that provides display and interaction with model results as well as visualization of biodiversity trends. The three elements of our proposed study lead from canopy characterization to mast production by individual trees to continent scale prediction of mast and the consumers that depend on it, jointly as a community. First, we characterize canopy condition at NEON locations, where hyperspectral and lidar data are paired with mast-production and consumer data. Second, new canopy condition variables enter as covariates (predictors) for tree fecundity, including coarser-scale remotely-sensed drought stress indices (e.g., a new thermal stress index developed from MODIS LST (DroughtEye, 4 km), and NASA/JPL's new ECOSTRESS sensor for validation and downscaling). This second step uses MASTIF and Generalized Joint Attribute Modeling (GJAM) for joint community prediction. Third, we use US Forest Inventory and Analysis plots with the fitted MASTIF to project mast production nationally. This third step engages dimension reduction techniques developed specifically for dependence in large spatio-temporal arrays. Finally, all canopy condition and drought indices, habitat, and mast predictors are used to predict consumer abundances nationally. These predictive distributions include conditional prediction of rare species to reevaluate ecological forecasting for conservation goals. We will provide public access to our results and visualization of the NEON biodiversity trends, indices of ecosystems that characterize community and physical habitat structure, energy flow, herbivore food webs, and their vulnerability to climate change by drought stress (http://pbgjam.org). Products of this analysis will take the form of i) software for dimension reduction in ecological forecasting, available as an R package, ii) remotely sensed canopy condition and drought-stress maps iii) community predictions of biodiversity change and iv) conditional prediction of threatened species. The goals of this project parallel those of The Decadal Survey in tracking flows of energy and changes in ecosystem structure, function and biodiversity, and relying on national field data collection and space-borne NASA mission data.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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