Natural Resources, School of


Seasonal Variation in the NDVI–Species Richness Relationship in a Prairie Grassland Experiment (Cedar Creek)

Ran Wang, University of Alberta
John A. Gamon, University of Nebraska - Lincoln
Rebecca A. Montgomery, University of Minnesota
Philip A. Townsend, University of Wisconsin-Madison
Arthur I. Zygielbaum, University of Nebraska-Lincoln
Keren Bitan, University of Minnesota
David Tilman, University of Minnesota, St. Paul
Jeannine Cavender-Bares, University of Minnesota

Document Type Article

Copyright 2018 by the authors.

Open access



Species richness generally promotes ecosystem productivity, although the shape of the relationship varies and remains the subject of debate. One reason for this uncertainty lies in the multitude of methodological approaches to sampling biodiversity and productivity, some of which can be subjective. Remote sensing offers new, objective ways of assessing productivity and biodiversity. In this study, we tested the species richness–productivity relationship using a common remote sensing index, the Normalized Difference Vegetation Index (NDVI), as a measure of productivity in experimental prairie grassland plots (Cedar Creek). Our study spanned a growing season (May to October, 2014) to evaluate dynamic changes in the NDVI–species richness relationship through time and in relation to environmental variables and phenology. We show that NDVI, which is strongly associated with vegetation percent cover and biomass, is related to biodiversity for this prairie site, but it is also strongly influenced by other factors, including canopy growth stage, short-term water stress and shifting flowering patterns. Remarkably, the NDVI-biodiversity correlation peaked at mid-season, a period of warm, dry conditions and anthesis, when NDVI reached a local minimum. These findings confirm a positive, but dynamic, productivity–diversity relationship and highlight the benefit of optical remote sensing as an objective and non-invasive tool for assessing diversity–productivity relationships.