The Terrestrial Biogeochemistry Laboratory in the Department of Earth and Environmental Science of the University of Pennsylvania invites applicants for competitive Ph.D. fellowships in soil carbon biogeochemistry. Projects seek to quantify and characterize the recalcitrant pools of pyrogenic and geogenic carbon in soils. We’ll examine chemical transformations and dissolution as mechanisms controlling their fate in the critical zone. Positions are ideal for candidates who have completed an MSc in soil science, geosciences, environmental chemistry, ecosystem science or related fields, but outstanding BSc graduates in such programs will be considered. For further information, please contact Dr. Alain Plante by email (firstname.lastname@example.org) or consult the department website (www.sas.upenn.edu/earth). Applications can be submitted directly online (https://www.applyweb.com/upenng/) before December 15.
Ramped pyrolysis/oxidation (RPO), known colloquially as “dirt burning”, involves ramped heating of sediment or soil samples to generate gases suitable for capture and radiocarbon analysis. RPO radiocarbon analysis has been used to discern 14C age spectra and carbon dynamics in sediments with a variety of organic carbon sources. A recent NSF-funded workshop on Thermal Analysis of Natural Organic Matter held Sept. 15-16, 2016 at the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility at the Woods Hole Oceanographic Institute (WHOI) brought a variety of researchers together to explore other potential applications of RPO. A combination of oceanographers, soil scientists, biogeochemists, and biologists convened to discuss the technique, which has emerged out of its infancy and into a nebulous adolescence. The core objectives of the workshop included: 1) Facilitating discussion between those using and interested in RPO, 2) Discussing how current RPO applications have increased understanding of Earth systems, 3) Targeting technical improvements and methodological experiments, and 4) Defining issues that would benefit from development and application of RPO. Discussion centered on geoscience and technical questions. For biogeochemistry, future endeavors for RPO can focus on discerning soil age distributions, bioreactor kinetics, and analysis of old ocean (and potentially freshwater) DOC. Technical questions involved refining RPO’s assessment of 14C content more directly with 14C age. Attendees also questioned how RPO can help assess the effect of chemical treatment on sample analysis. The technique is of particular interest to my dissertation research. The Plante lab frequently uses ramped combustion for the characterization of soil organic matter. I intend to couple those methods with 14C analyses on a set of African Dark Earth samples to examine the stability of organic matter in these soils.
Here is the third and last installment on my themed set of posts. In part 1, I reported on how my sabbatical visit to New Zealand grew into a three-way collaboration between labs in New Zealand, the US and Canada. In part 2, I described a conceptual framework that the Powell Center working group on soil carbon came up with to guide our thinking about the next generation models for soil carbon cycling. Here, I’ll describe how data, code and results are fused together in how we generate results from thermal analysis experiments.
Like many other analytical methods, the thermal analysis techniques we apply to soil organic matter research are relatively easy to execute. That is, running a sample is easy. The challenge after running the sample, in getting from raw data to an interpretable result. There are a number of steps to be taken to get from raw data to a results, most of which now involve code. This summer, I’ve moving, slowly but surely, to making the process open and reproducible as much as possible. Having read Bond-Lamberty et al. (2016), Wilson et al. (submitted) and Wilson et al. (2014), among others, I’m beginning to see the path forward.
But first, a brief history lesson. When we first started performing analytical thermal analysis, we stored our data in the usual chaotic way. At least the projects got their own folder, but all data management was done by hand using proprietary software and spreadsheets. We then hired someone for 6 months to write a huge amount of code to engineer a postGRE and R-based database system for data storage and manipulation that could, in theory be accessed publicly on a server. Unfortunately, that system was so complex that when it was “broken” by a change in the instrument software’s file format, it was deemed obsolete. We then moved to using smaller chunks of code using R scripts for most of the data manipulation tasks, but there are still a few steps that use propriety/commercial software.
The raw data from analytical thermal analysis consists of six data streams (time, temperature, mass, heat flux, CO2 evolved and H2O evolved) from two different instruments that don’t “speak” to each other. Each of these data streams consists of 5,400 to 20,000 data points. They generate several qualitative thermograms (plots as a function of temperature), but the real power of the analyses comes when they can be quantified and statistically compared to each – and this requires code to convert the raw data, to “tidy” data, to merged, final data, to results in the form of summary indices and statistical comparisons.
Aside from a growing consensus about best practices, the impetus for moving to more open and reproducible data management is that I recently purchased new instrumentation that will substantially increase our sample throughput. The historical accumulation of data, and the growing demands on the method, present a substantial challenge for data management. The goal now is to be able to post raw data and allow our collaborators to perform their own data manipulations according to a sequence of reproducible steps recommended/prescribed by us. This will require a major change in how we organize our own data, the development of rigorously tested and reproducible means for data manipulation, and new demands on our collaborators for how they handle their data. The current vision is the use of a repository system (e.g., GitHub) for storing data (both raw and tidy), metadata, and scripts, and for version control of the data and scripts. We would post raw data to the repository for access by a collaborator, and provide the documentation and scripts as a recommended means of converting the raw data to usable data to results.
In part 1, I reported on my sabbatical visit to New Zealand grew into a three-way collaboration between labs in New Zealand, the US and Canada. Here in part 2, I’ll describe one conceptual framework that the Powell Center working group on soil carbon came up with to guide our thinking about the next generation models for soil carbon cycling. And in part 3 (coming soon), I’ll describe how data, code and results are fused together in ongoing projects.
Part 2: Concepts, measurements, models
I currently have the pleasure of working with 14 other great researchers on a USGS-funded Powell Center Working Group project called “What lies below? Improving quantification and prediction of soil carbon storage, stability, and susceptibility to disturbance”, led by Corey Lawrence (USGS), Kate Heckman (US Forest Service), Marco Keiluweit (UMass) and Susan Crow (University of Hawaii). According to their website, “the Powell Center serves as a catalyst for innovative thinking in Earth system science research by providing the time, creative space, and computational, data manipulation and data management resources to promote synthesis of existing information and emergent knowledge”, and our working group project seeks to “synthesize information on the processes controlling soil C storage across different spatial scales and develop new procedures to translate local measurements to the regional and global scale datasets used by models”. We met for an intensive week in early May at the Powell Center in Fort Collins, CO.
One of the conceptual frameworks that the group intends to pursue is the union of concepts, measurements and models. The premise here is that our current models of soil C cycling (like Century) have matured well, and are quite robust because they adequately balance concepts, measurements and modeled processes. There are, in a sense, balanced or equilateral triangles. However, a new generation of models seek to be more mechanistic and better able to predict soil C responses to disturbances such as climate change. These new models can be loosely grouped into several efforts that have implicit strengths in concepts, measurements or modeled processes. Analogously, these models are unbalanced triangles. For example, there is a vast (and growing) amount of data and measurements on the molecular biology of the soil microbial community, but a weaker understanding of how this genetic diversity translates to soil C cycling processes, or how these data might be incorporated into next-generation models. Or, we understand conceptually that soil mineralogy has a profound effect on the formation of organo-mineral associations that stabilize organic C, and we have a large number of measurements of C and mineralogical characteristics. However, our models frequently only use soil clay content (if anything at all) to represent mineral stabilization of organic matter. Our Powell Center Working Group has tasked itself to explore and illustrate these imbalanced triangles as a way to identify knowledge gaps, and paths forward to the development of new, robust and mature models.
As a first year PhD student, the Summer Soil Institute (SSI) at Colorado State University was an excellent opportunity to learn from and network with top soil scientists. The SSI is one of the leading programs for soil science education in the country. It provides a venue to formally learn valuable soil analysis techniques that can be applied to participants’ research. The curriculum for the two-week course involved field sampling, sample processing, and a broad and informative series of lectures on soil pedology, chemistry, microbiology, and gas fluxes. I participated in this year’s program, and described my experience in a detailed two-part story on my blog. Part I (here) describes my first week’s experience in soil sample collection and chemical analyses, while Part II (here) covers the second week’s experience in soil biological analyses.
I grew up in Beijing and got my Bachelor’s and Master’s degrees from Peking University. My research interests are the interactions between forest carbon cycle and global environmental changes at multiple spatial and temporal scales. I arrived at University of Pennsylvania at 2011, and started to work with Dr. Fred Scatena. After he passed away suddenly in 2013, I transferred to Dr. Alain Plante’s lab. During my time in Plante’s lab, I completed my PhD project about the forest biomass and soil carbon stocks changes in the Delaware River Basin. This project was started from 2001, when 77 plots in three research area were established. I led a field crew with investigators from US Forest Service and Penn students, revisiting 61 of the plots, measuring forest biomass and collecting soil samples using two sampling methods. Forest biomass data from two field surveys were used to address the linkage between forest biomass C stock change and tree demographic change over the past decade. Two soil sampling methods were compared to reduce the uncertainties of soil CN content estimations. Based on the results of field measurements, a process-based ecosystem model (PnET-CN) was parameterized and used to simulate the spatial distribution of forest C pool and fluxes, and to qualify the impacts of environmental changes on forest C cycle. This project was supported by US Forest Service, and co-advised by Dr. Yude Pan. In the past five years, I had a very good time studying above and below- ground forest carbon cycles in the field, the lab and in the digital world. In the next few years, I will work as a postdoctoral fellow with Dr. Altaf Arain at McMaster University in Hamilton, Ontario, Canada, and go on exploring forest carbon dynamics using micrometeorology and remote sensing technique.