Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

Authors

Jeannine Cavender-Bares, University of Minnesota - Twin CitiesFollow
Anna K. Schweiger, University of Minnesota - Twin Cities
John Gamon, University of Nebraska-LincolnFollow
Hamed Gholizadeh, University of Nebraska-LincolnFollow
Kimberly Helzer, University of Minnesota - Twin Cities
Cathleen Lapadat, University of Minnesota - Twin Cities
Michael D. Madritch, Appalachian State University
Philip A. Townsend, University of Wisconsin-Madison
Zhihui Wang, University of Wisconsin-Madison
Sarah E. Hobbie, University of Minnesota - Twin Cities

ORCID IDs

Jeannine Cavender-Bares https://orcid.org/0000-0003-3375-9630

Anna K. Schweiger https://orcid.org/0000-0002-5567-4200

John A. Gamon https://orcid.org/0000-0002-8269-7723

Hamed Gholizadeh https://orcid.org/0000-0002-4770-7893

Michael D. Madritch https://orcid.org/0000-0003-2562-3135

Philip A. Townsend https://orcid.org/0000-0001-7003-8774

Zhihui Wang https://orcid.org/0000-0003-1064-7820

Sarah E. Hobbie https://orcid.org/0000-0001-5159-031X

Date of this Version

2022

Comments

Cavender-Bares, J., A. K. Schweiger, J. A. Gamon, H. Gholizadeh, K. Helzer, C. Lapadat, M. D. Madritch, P. A. Townsend, Z. Wang, and S. E. Hobbie. 2022. Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments. Ecological Monographs 92(1):e01488. 10.1002/ecm.1488

Abstract

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy datawere used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.