Dirty pool: Soil’s large carbon stores could be freed by increased CO2, plant growth (Nature Climate Change)

By Morgan Kelly, Office of Communications

Soil carbon
Researchers based at Princeton University report that an increase in human-made carbon dioxide in the atmosphere could initiate a chain reaction between plants and microorganisms that would unsettle one of the largest carbon reservoirs on the planet — soil. The researchers developed the first computer model to show at a global scale the complex interaction between carbon, plants and soil. The model projected changes (above) in global soil carbon as a result of root-soil interactions, with blue indicating a greater loss of soil carbon to the atmosphere. (Image by Benjamin Sulman, Princeton Environmental Institute)

An increase in human-made carbon dioxide in the atmosphere could initiate a chain reaction between plants and microorganisms that would unsettle one of the largest carbon reservoirs on the planet — soil.

Researchers based at Princeton University report in the journal Nature Climate Change that the carbon in soil — which contains twice the amount of carbon in all plants and Earth’s atmosphere combined — could become increasingly volatile as people add more carbon dioxide to the atmosphere, largely because of increased plant growth. The researchers developed the first computer model to show at a global scale the complex interaction between carbon, plants and soil, which includes numerous bacteria, fungi, minerals and carbon compounds that respond in complex ways to temperature, moisture and the carbon that plants contribute to soil.

Although a greenhouse gas and pollutant, carbon dioxide also supports plant growth. As trees and other vegetation flourish in a carbon dioxide-rich future, their roots could stimulate microbial activity in soil that in turn accelerates the decomposition of soil carbon and its release into the atmosphere as carbon dioxide, the researchers found.

This effect counters current key projections regarding Earth’s future carbon cycle, particularly that greater plant growth could offset carbon dioxide emissions as flora take up more of the gas, said first author Benjamin Sulman, who conducted the modeling work as a postdoctoral researcher at the Princeton Environmental Institute.

“You should not count on getting more carbon storage in the soil just because tree growth is increasing,” said Sulman, who is now a postdoctoral researcher at Indiana University.

On the other hand, microbial activity initiated by root growth could lock carbon onto mineral particles and protect it from decomposition, which would increase long-term storage of carbon in soils, the researchers report.

Whether carbon emissions from soil rise or fall, the researchers’ model depicts an intricate soil-carbon system that contrasts starkly with existing models that portray soil as a simple carbon repository, Sulman said. An oversimplified perception of the soil carbon cycle has left scientists with a glaring uncertainty as to whether soil would help mitigate future carbon dioxide levels — or make them worse, Sulman said.

“The goal was to take that very simple model and add some of the most important missing processes,” Sulman said. “The main interactions between roots and soil are important and shouldn’t be ignored. Root growth and activity are such important drivers of what goes on in the soil, and knowing what the roots are doing could be an important part of understanding what the soil will be doing.”

The researchers’ soil-carbon cycle model has been integrated into the global land model used for climate simulations by the National Oceanic and Atmospheric Administration’s (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) located on Princeton’s Forrestal Campus.

Read the abstract

Benjamin N. Sulman, Richard P. Phillips, A. Christopher Oishi, Elena Shevliakova, and Stephen W. Pacala. 2014. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change. Arti­cle pub­lished in December 2014 print edition. DOI: 10.1038/nclimate2436

The work was supported by grants from NOAA (grant no. NA08OAR4320752); the U.S. Department of Agriculture (grant no. 2011-67003-30373); and Princeton’s Carbon Mitigation Initiative sponsored by BP.


Computational clues into the structure of a promising energy conversion catalyst (J. Physical Chemistry Letters)

Mosaic structure
Representation of the mosaic texture of β-NiOOH and its possible structures.

By Tien Nguyen, Department of Chemistry

Hydrogen fuel is a promising source of clean energy that can be produced by splitting water into hydrogen and oxygen gas with the help of a catalyst, a material that can speed up the process. Although most known catalysts are inefficient, one called iron-doped nickel oxide is promising but not well understood.

Now researchers at Princeton University have reported new insights into the structure of an active component of the nickel oxide catalyst, known as β-NiOOH, using theoretical calculations. Led by Annabella Selloni, professor of chemistry at Princeton, the findings were published in The Journal of Physical Chemistry Letters on October 28.

“Understanding the structure is the basis for any further study of the material’s properties. If you don’t know the material’s structure you can’t know what it’s doing,” Selloni said. Nickel oxide’s exact structure has been difficult to determine experimentally because it is constantly changing during the reaction.

The research team took a theoretical approach and employed a “genetic algorithm” to search for the structure. Genetic algorithms operate under a set of parameters that draw inspiration from evolution by creating generation after generation of structures to arrive at the most “fit” or most likely candidates.

Taking the results of the genetic algorithm search in combination with computational techniques known as hybrid density functional theory calculations—which estimate a molecule’s electronic structure—co-author Ye-Fei Li, a former postdoctoral researcher at Princeton who is now at Fudan University, and Selloni were able to identify structures of nickel oxide that supported existing observations.

One such observation is the material’s mosaic texture, composed of tiny grain-like microstructures. The researchers propose that these microstructures are stable tunnel structures that relieve stress between layers. Another observed feature is the doubling of the distance between layers made of the same material, referred to as its c axis periodicity, which represents the alternating layers of Ni(OH)2 and NiO2 formed during the reaction.

Armed with a better understanding of the material’s structure, the scientists hope to further map out its activity in the reaction. “I’m interested in the microscopic mechanisms, what are the electrons and atoms doing?” Selloni said.

Read the abstract

Li, Ye-Fei and Annabella Selloni. “Mosaic Texture and Double c-Axis Periodicity of β–NiOOH: Insights from First-Principles and Genetic Algorithm Calculations.” J. Phys. Chem. Lett. 2014, 5, 3981.

This work was supported by the US Department of Energy, Division of Chemical Sciences, Geosciences and Biosciences under award DE-FG0212ER16286.

Caught in the act: Video system for mapping behaviors (Royal Society: Interface)

By Catherine Zandonella, Office of the Dean for Research

Studies of animal behavior have come a long way from the days when scientists followed their subjects around with pen and notepad. But although cameras have replaced clipboards, evaluating the resulting videos is still a cumbersome process.

To make that job easier and more comprehensive, researchers at Princeton have built a computerized system that analyzes videos to reveal what animals do, how often and for how long, and then generates an easy-to-understand map of the behaviors.

By looking at the map, the researchers can tell what sorts of movements the animal did without having to spend time watching the video. They used the system to track the behavior of fruit flies (Drosophila melanogaster) and found that it accurately detected behaviors such as grooming a leg or waggling a wing.

Map that indicates the type of movements of a fly.
Princeton researchers have developed a computerized system for evaluating animal behavior. A video system records an animal, in this case a fruit fly, for one hour. Then a computer program analyzes the video to create a map of the behaviors. The map can reveal, for example, the differences in movements between male and female fruit flies. (Image source: Joshua Shaevitz)

The researchers’ goal was a system that could create accurate records of behaviors for use in studies of the mechanisms behind those behaviors – in other words, the genes and brain circuits that govern movements. Knowing which genes and neural circuits govern behavior will help answer basic scientific questions and could shed light on the mechanisms behind conditions such as autism.

Described in the journal The Royal Society Interface, the system was built for monitoring Drosophila flies, which are commonly used in studies of genes, but the researchers say that the system can also analyze the movements of other creatures, including worms, mice and humans.

The new system is a significant advance over current approaches because it takes note of all the animal’s activities, not just behaviors that seem important to researchers, said Joshua Shaevitz, associate professor of physics and the Lewis-Sigler Institute for Integrative Genomics, who led the study. “We don’t know which behaviors are important to a fruit fly, or a mouse,” he said. “Instead, we ask the computer to find behaviors that are frequently repeated, which tend to be the ones that are worth studying.”

Researchers need reliable ways to catalog behaviors in studies of how genes and neural pathways control behavior. A common experiment involves deleting a gene in an organism such as a fruit fly or worm to look for any resulting loss of function or change in the organism’s behavior. But without reliable behavioral data, researchers cannot make firm conclusions about the role of the deleted gene.

With the new system, a high-speed camera records the animal for a given period of time, and the computer sorts the resulting video frames. For the current study, the researchers recorded individual fruit flies for a period of one hour, resulting in an enormous amount of data. The challenge was how to sort the frames and translate the images into data that could be evaluated.

“We have to figure out how the body parts of the fly are positioned in relation to each other at each point in time, and then find a way to tell how those body parts are moving, while keeping the amount of data manageable using the available computing power,” said Gordon Berman, an associate research scholar in the Lewis-Sigler Institute for Integrative Genomics. “Then you have to project the information into some form that allows you to understand the data.”

The researchers accomplished this goal by writing a computer program that converts the information about a fly’s position and movements into mathematical descriptions that are then grouped according to their similarities. The computer places these groupings on a two-dimensional map, so that different regions of the map represent different types of movements.

A computerized system for evaluating animal behavior.
Individual fruit flies were filmed for about one hour, resulting in over 300,000 video frames. Then, a computer program converts the fly’s position in each frame into mathematical descriptions that represent movements. The computer then groups the movements on a map, so that one region of the map represents leg movements while another represents wing movements, and so on. By looking at the map, the researchers can tell what sorts of movements the fly did, such as grooming its front leg, grooming its head, or waggling its wing. (Image source: Joshua Shaevitz)

To verify that the system worked, the researchers collected data from 50 male and 50 female flies. They identified hundreds of behaviors, including ones that had never been documented and others that were consistent with findings from human observers. For example, some of the findings consistent with previous observations were that male fruit flies kick out their legs when grooming, whereas females don’t, and that young females are more active than young males. “Males and females do things slightly differently, and we can pick up on that right away,” said Shaevitz.

Male fruit flies kick out with their legs when grooming:

whereas females do not:

The team is now expanding the capabilities of the method, Shaevitz said. “The technique that we have developed can also be used to study patterns of behavior in humans,” Shaevitz said. “There are several conditions that are diagnosed on the basis of behavior, such as autism. Having a more quantitative way of measuring behavior could improve the accuracy of diagnoses.”

The study co-authors included William Bialek, Princeton’s John Archibald Wheeler/Battelle Professor in Physics and the Lewis-Sigler Institute for Integrative Genomics, and Daniel Choi, a graduate student working with Shaevitz.

The work was funded through awards from the National Institutes of Health (GM098090, GM071508), the National Science Foundation (PHY 0957573, PHY 1066293), the Pew Charitable Trusts, the Swartz Foundation and the Alfred P. Sloan Foundation.

Read the abstract

Gordon J. Berman, Daniel M. Choi, William Bialek, Joshua W. Shaevitz. Mapping the stereotyped behaviour of freely moving fruit flies. The Journal of the Royal Society Interface (2014). DOI: 10.1098/rsif.2014.0672 Published online 20 August 2014 http://rsif.royalsocietypublishing.org/content/11/99/20140672