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

Authors

Ran Wang, University of AlbertaFollow
John A. Gamon, University of Nebraska - LincolnFollow
Rebecca A. Montgomery, University of MinnesotaFollow
Philip A. Townsend, University of Wisconsin-MadisonFollow
Arthur I. Zygielbaum, University of Nebraska-LincolnFollow
Keren Bitan, University of MinnesotaFollow
David Tilman, University of Minnesota, St. PaulFollow
Jeannine Cavender-Bares, University of MinnesotaFollow

Document Type

Article

Date of this Version

2016

Citation

Remote Sens. 2016, 8, 128

Comments

© 2016 by the authors

Open access

doi:10.3390/rs8020128

Abstract

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.