Gavin McNicol, PhD

Research Themes:

We explore the connection between terrestrial biogeochemistry and the Earth’s climate system. We focus on carbon cycling in wet environments spanning different human disturbance and management conditions, such as wetlands, temperate rainforests, and waste systems. We use a mix of methods to measure, understand, and predict biogeochemical processes, combining soil science, ecosystem ecology, and Earth systems science. In the laboratory, we manipulate soil conditions to understand the microscale controls of processes like decomposition and rates of greenhouse gas emissions. In the field, we make long-term measurements of gas exchanges to quantify the unique greenhouse gas signatures of wet ecosystems and how they respond to land-use and global change. At regional to global scales, we use data science and machine learning to synthesize big data from global scientific and sustainable development networks. Current collaborators include eddy covariance flux scientists from AmeriFlux (FLUXNET-CH4), an NSF-funded coastal rainforest margin’s research coordination network (CRMRCN), and SOIL, a research and development non-governmental organization providing sanitation services in Haiti.

Courses:

EAES 420 Data Science and Statistics

We in the Earth and Environmental Sciences (EaES) are feeling the effects of rapid technological change. In the last couple of decades, our ability to acquire long-term, continuous, and high-frequency environmental measurements – even in remote locations – has improved radically. These advances resolve old problems of data scarcity and present new opportunities to study the earth. However, to take full advantage of “big” data, we need to become better data scientists. This course will introduce you to fundamentals of coding in the free and widely used programming language ‘R’. Through hands-on classroom programming and interactive data analysis, you will become familiar with the data science workflow including how to import, tidy, visualize, analyze, and communicate big EaES datasets, including geospatial data, and how to use version control to ensure your work is reproducible, a valued skill in both private industry and academic research. The course will also cover fundamental statistics including data distributions, descriptive statistics, and linear regression. You will need access to a personal computer but there are no math or computer science (coding) prerequisites for this class.

EAES 579 Climate and Ecosystem Science

This course will explore contemporary global climate change using the knowledge and methods of ecosystem science. Students will study the biogeochemical regulation of the global climate system and will focus on important mechanisms organized across a hierarchy of scales (i.e., soil microbial, to vegetation canopy, to landscape patchwork, etc.). Course content includes lecture, laboratory experiments and/or field work, geospatial data analysis, reading of primary literature and select textbook chapters, group discussions, and oral presentations.

Complete list of publications on Google Scholar

Wetland Methane

• McNicol G. et al. (2023) Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4v1.0): Model development, network assessment, and budget comparison. AGU Advances, https://doi.org/10.1029/2023AV000956
• Delwiche K. B. Knox S. H. Malhotra A. Fluet-Chouinard E. McNicol G. et al. (2021) FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands. Earth Syst. Sci. Data, https://doi.org/10.5194/essd-2020-307
• Knox S. H. Bansal S. McNicol G. Schafer K. Sturtevant C. Ueyama M. Valach A.C. et al. (2021) Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales. Global Change Biology, https://doi.org/10.1111/gcb.15661
• McNicol G. Knox S. Guilderson T. Baldocchi D. D. & Silver W. L. (2020) Where old meets new: An ecosystem study of methanogenesis in a reflooded agricultural peatland. Global Change Biol., https://doi.org/26:772-785. 10.1111/gcb.14916
• Yang W. H. McNicol G. Teh Y. A. Estera K. Wood T. E. & Silver W. L. (2017) Evaluating the classical versus an emerging conceptual model of peatland methane dynamics. Global Biogeochem. Cycles, 31: 1435-1453. https://doi.org/10.1002/2017GB005622
• McNicol G. Sturtevant C. Knox S. Dronova I. Baldocchi D. D. & Silver W. L. (2017) Effects of seasonality, transport pathway, and spatial structure on greenhouse gas fluxes in a restored wetland. Global Change Biol., 23: https://doi.org/2768-2782. 10.1111/gcb.13580

Soil Carbon

• McNicol G. et al. (2023) Small, coastal temperate rainforest watersheds dominate dissolved organic carbon transport to the Northeast Pacific Ocean. Geophysical Research Letters, https://doi.org/10.1029/2023GL103024
• Lawrence C. R. Beem-Miller J. Hoyt A. M. Monroe G. Sierra C. A. Stoner S. Heckman K. Blankinship J. C. Crow S. E. McNicol G. et al. (2020) An open source database for the synthesis of soil radiocarbon data: ISRaD version 1.0. Earth Syst. Sci. Data, 12: 61–76. https://doi.org/10.5194/essd-12-61-2020
• McNicol G. Bulmer C. D’Amore D. Sanborn P. Saunders S. Giesbrecht I. Gonzalez Arriola S. Bidlack A. Butman D. & Buma B. (2019) Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest. Env. Res. Letters, 14: 014004. https://doi.org/10.1088/1748-9326/aaed52

Sanitation and Climate Change

• McNicol G. Jeliazovski J. François J. J. Kramer S. & Ryals R. Climate change mitigation in sanitation via offsite composting of human waste. Nat. Climate Change, 10: 545-549. 1038/s41558-020-0782-4

BS Environmental Science, 2010, Stirling University
PhD Environmental Science, Policy and Management, 2016, University of California Berkeley