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Related Experiment Video

Updated: Sep 4, 2025

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

JenaTron - An Experimental Approach to Study the Effects of Plant History and Soil History on Grassland Ecosystem Functioning

Published on: March 21, 2025

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

Jeannine Cavender-Bares¹, Anna K Schweiger^1,2,3, John A Gamon^4,5,6

¹Department of Ecology, Evolution, and Behavior University of Minnesota Saint Paul Minnesota 55108 USA.

Ecological Monographs

|July 22, 2022

Summary

Remote sensing of vegetation can predict belowground soil processes in grasslands. Aboveground biomass is key in low-diversity systems, while plant community composition matters more in high-diversity systems.

Keywords:

biodiversity ecosystem processes hyperspectral data imaging spectroscopy microbial biomass phylogenetic‐functional groups plant traits productivity remote sensing soil enzyme activity soil processes vegetation chemistry

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Last Updated: Sep 4, 2025

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Published on: March 21, 2025

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Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Published on: July 24, 2018

Area of Science:

Ecology
Remote Sensing
Soil Science

Background:

Aboveground and belowground vegetation are linked, influencing soil microbial processes and nutrient cycling.
Remote sensing of vegetation offers potential for studying belowground ecosystem functions.

Purpose of the Study:

To test if remotely sensed vegetation data can predict belowground plant and soil processes in grassland experiments.
To investigate the role of vegetation quantity versus quality in driving belowground processes across different productivity levels.

Main Methods:

Utilized airborne imaging spectroscopy to map vegetation properties like biomass, cover, and community composition.
Analyzed relationships between image-derived variables and soil carbon, nitrogen, microbial communities, enzyme activity, and nitrogen mineralization.

Main Results:

In a low-productivity grassland (BioDIV), remotely sensed vegetation cover and biomass predicted soil respiration, microbial biomass, enzyme activity, and microbial composition.
In a high-productivity grassland (Wood River), remotely sensed vegetation composition (functional, chemical, phylogenetic) predicted belowground enzyme activity, microbial biomass, and nitrogen mineralization, but biomass did not.

Conclusions:

Remotely sensed vegetation quantity (biomass) and quality (composition) can predict belowground soil processes in grasslands.
The relative importance of quantity versus quality depends on ecosystem productivity and diversity.