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

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

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

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

Selective Microbial Genomic DNA Isolation Using Restriction Endonucleases

<div><p>To improve the metagenomic analysis of complex microbiomes, we have repurposed restriction endonucleases as methyl specific DNA binding proteins. As an example, we use DpnI immobilized on magnetic beads. The ten minute extraction technique allows specific binding of genomes containing the DpnI G^m6ATC motif common in the genomic DNA of many bacteria including γ-proteobacteria. Using synthetic genome mixtures, we demonstrate 80% recovery of <i>Escherichia coli</i> genomic DNA even when only femtogram quantities are spiked into 10 µg of human DNA background. Binding is very specific with less than 0.5% of human DNA bound. Next Generation Sequencing of input and enriched synthetic mixtures results in over 100-fold enrichment of target genomes relative to human and plant DNA. We also show comparable enrichment when sequencing complex microbiomes such as those from creek water and human saliva. The technique can be broadened to other restriction enzymes allowing for the selective enrichment of trace and unculturable organisms from complex microbiomes and the stratification of organisms according to restriction enzyme enrichment.</p></div

DpnI pulls down genomic DNA from different organisms with varying efficiency.

<p>*Recovery as compared to input by qPCR.</p><p>-Less than 2%, +/-2–10%, +10–50%, ++50–100%.</p><p>DpnI pulls down genomic DNA from different organisms with varying efficiency.</p

NGS data of creek samples.

<p>Pairwise plots of sample fractions versus input. (A) Bound vs. input. (B) Unbound vs. input. Plotted points are identifying reads of genera arbitrarily normalized to 10 M reads total.</p

Efficiency and range of DpnI pull-down.

<p>(A) Immobilized DpnI was incubated with a mixture of <i>E. coli</i> and human DNA for varying amounts of time. 40% of <i>E. coli</i> DNA binding occurs on less than one minute. Less than 0.2% of human DNA binds to DpnI. (B) Immobilized DpnI was incubated with <i>E. coli</i> DNA in buffers with pH of 4, 8 or 10. Almost all <i>E.coli</i> DNA was recovered in the range of pH tested. (C) A fixed amount of human DNA (1 µg) was mixed with decreasing levels of <i>E. coli</i> DNA and then incubated with immobilized DpnI. Approximately 80% of <i>E. coli</i> DNA is recovered down to levels of 10 fg. All data shown is the average of three experiments. (D) A fixed amount of <i>E. coli</i> DNA (1 ng) was mixed with increasing amounts of human DNA and then incubated with immobilized DpnI. There is a slight decrease in the recovery of <i>E. coli</i> DNA with increasing amounts of human DNA. However, even when human DNA is present at 10,000x, DpnI recovers over 70% of <i>E. coli</i> DNA.</p

NGS data of saliva samples.

<p>(A) Donut plots depicting relative abundance of identifying reads for microbial and human genomes in input, bound and unbound samples. (B, C) Pairwise plots of sample fractions versus input. (B) Bound vs. input. (C) Unbound vs. input. Plotted points are identifying reads of genera. To facilitate direct visual comparison between samples reads were normalized to 10 M total.</p

DpnI enriches prokaryotic DNA as determined by NGS.

<p>(A) NGS reads from the input, bound and unbound fractions of the synthetic mix. Reads from the input map overwhelmingly to human and rice, with less than 10% mapping to prokaryotes in the synthetic mixture. Less than 10% of the reads from the bound fraction map to human with the majority mapping to <i>E. coli</i>. (B) DamMT+ genomes are enriched 30 to 70-fold versus their input levels, and 300 to 800-fold versus human DNA. (C) Genomes that lack DamMT are enriched when compared to human and rice.</p

NGS coverage maps for <i>E. coli</i> from input (A) and bound (B) fractions of the synthetic mixture.

<p>Reads were mapped to <i>E. coli</i> O157:H7 EDL933 and binned into 1000 bp bins. (A) The average depth of coverage is 0.5 for <i>E. coli</i> in the input fraction (green), with 62% of the genome covered. (B) For the bound fraction (blue), the average depth of coverage increases to 60 and 99.5% of the genome is covered. The input fraction (green) is also plotted here for comparison to the bound fraction at the same scale.</p