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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 data were 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.
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Labile Soil Carbon Heterogeneity Driven by Consumer Engineering of Aboveground Structure in a Kenyan Savanna
Abstract Aboveground ecosystem structure moderates and even confers essential ecosystem functions. This includes an ecosystem’s carbon dynamics, which are strongly influenced by its structure: for example, tropical savannas like those in central Kenya store substantial amounts of carbon in soil. Savannas’ belowground allocation of carbon makes them important for global carbon sequestration, but difficult to monitor. However, the labile soil carbon pool is responsive to changes in ecosystem structure and is thus a good indicator of overall soil organic carbon dynamics. Kenya’s savanna structure is controlled by belowground ecosystem engineers (termites), ambient weather conditions, and the aboveground engineering influences of large-bodied, mammalian consumers. As a result, climate change and biodiversity loss are likely to change savannas’ aboveground structure. To predict likely outcomes of these threats on savanna soil carbon, it is critical to explore the relationships between labile soil carbon and ecosystem structure, local climate, and mammalian consumer community composition. In a large-scale, long-term herbivore exclosure experiment in central Kenya, we sampled labile carbon from surface soils at three distinct savanna structural elements: termite mounds, beneath tree canopies, and the grassland matrix. In one sampling year, we measured total extractable organic carbon (TEOC), total extractable nitrogen (TEN), and extractable microbial biomass for each sample. Across three sampling years with varying weather conditions, we measured rate of labile soil carbon mineralization. We quantified areal coverage of each structural element across herbivore community treatments to estimate pool sizes and mineralization dynamics at the plot scale. Concentrations and stocks of soil TEOC, TEN, and microbial biomass were driven by the structural element from which they were sampled (soils collected under tree canopies generally had the highest of each). Large-bodied herbivore community composition interacted variably with concentrations, stocks, and carbon mineralization, resulting in apparently compensatory effects of herbivore treatment and structural element with no net effects of large herbivore community composition on plot-scale labile carbon dynamics. We confirmed engineering of structural heterogeneity by consumers and identified distinct labile carbon dynamics in each structural element. However, carbon and nitrogen were also influenced by consumer community composition, indicating potentially compensatory interacting effects of herbivore treatment and structural element. These results suggest that one pathway by which consumers influence savanna carbon is by altering its structural heterogeneity and thus the heterogeneity of its plot-scale labile carbon.
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- Award ID(s):
- 1931224
- PAR ID:
- 10565325
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Ecosystems
- Volume:
- 28
- Issue:
- 1
- ISSN:
- 1432-9840
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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