Tree Diversity - A Function of Gross Primary Production?

Joanne Michelle Nightingale, NASA GSFC, joanne.m.nightingale@nasa.gov (Presenting)
Weihong Fan, Richard Stockton College of New Jersey, weihong.fan@stockton.edu
Nicholas Coops, University of British Columbia, nicholas.coops@ubc.ca
Richard Waring, Oregon State University, richard.waring@oregonstate.edu

At the regional scale, ecologists have theorized that spatial variation in biodiversity can be interpreted as a response to differences in climate. To test this theory we assumed that ecological constraints associated with current climatic conditions might best be correlated with tree richness expressed through satellite-derived measures of gross primary production (GPP). To evaluate current patterns in tree diversity across the United States we acquired information on tree composition from the USDA Forest Service’s Forest Inventory and Analysis program that represented more than 174 000 survey plots. We selected 2693 cells of 1000 km2 within which a sufficient number of plots were available to estimate tree richness per hectare. Estimates of forest productivity varied from simple MODIS (Moderate Resolution Imaging Spectro-radiometer) vegetation indices at 16-d intervals, to 8- and 10-d GPP products derived with minimal climatic data (MODIS) and SPOT-Vegetation (Syste me Pour l’Observation de la Terre), to 3-PGS (Physiological Principles Predicting Growth with Satellites), which requires both climate and soil data. Across the United States, modeled predictions of gross productivity accounted for between 51% and 77% of the recorded spatial variation in tree diversity, which ranged from 2 to 67 species per hectare. Only 3-PGS predictions fit a theorized unimodal function by being able to distinguish highly productive forests in the Pacific Northwest that support lower than expected tree diversity. Other models predicted a continuous steep rise in tree diversity with increasing productivity, and did so with generally better or nearly equal precision with fewer data requirements.