R. Quinn Thomas

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Associate Professor | Virginia Tech | Data Science Faculty Fellow | Lead of Ecological Forecasting Initiative Research Coordination Network

Ecosystem Dynamics and Forecasting

Ecosystem: We study the forest and freshwater ecosystems upon which society depends

Dynamics:  We model how ecosystems change over time in response to land-use, climate change, atmospheric deposition, and management.  We measure carbon, water, and energy exchange between ecosystems and the atmosphere using eddy-covariance and biometeorology sensors.

Forecasting:  We predict the future of ecosystems by combining observations and ecosystem models using statistical techniques.

Data Science Faculty Fellow
Co-lead of the Ecological Forecasting Project at Virginia Tech
Department of Forest Resources and Environmental Conservation & Department of Biological Sciences
Virginia Tech

Steger Hall (0477) Room 315B
1015 Life Science Circle
Blacksburg, VA 24061
rqthomas@vt.edu
@rquinnthomas
Google Scholar
ORCID
GitHub

Projects

Ecological Forecasting Initiative Research Coordination Network (DEB-1926388)

Lead PI of an NSF sponsored 5-year project that is leading workshops, conferences, and collaborative software development to address the following objectives:

  1. Define community standards and best practices for developing, sharing, and archiving forecasts and models
  2. Increase the number and diversity of NEON-enabled (National Ecological Observatory Network) forecasts by developing and hosting the NEON Ecological Forecasting Challenge
  3. Create educational materials to empower scientists at all career stages to forecast using NEON data products
  4. Support the creation of software to produce NEON-enabled forecasts at intensive and collaborative coding-focused workshops
  5. Align forecast outputs and decision support with the needs of forecast users at mission-driven agencies to guide decision making, and
  6. Synthesize forecasts to examine how limits to forecastability vary across ecological systems and scales.

Other current projects

Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management (EF-2318861)

LTREB: Integrating real-time open data pipelines and forecasting to quantify ecosystem predictability at day to decadal scales (DEB-2327030)

Democratized Cyberinfrastructure for Open Discovery to Enable Research (OAC-2209866)

Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting (DBI-1933016)

Macrosystems EDDIE: An undergraduate training program in macrosystems science and ecological forecasting (DEB-1926050)

Global Centers Track 2: Building the Global Center for Forecasting Freshwater Futures (OISE-2330211)

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In the news

Scientists spurred by a thirst to transform the field of phytoplankton forecasting - VT News

Building the tools to make environmental data more accessible and forecasts more accurate - VT News

A vicious cycle of oxygen loss threatens water quality in lakes - VT News

Fewer ticks, cleaner water, and more carbon intake: Fellowship recipient Quinn Thomas plans to use data to predict our environment - VT News

Virginia Tech pioneers smart reservoirs - Roanoke Times

Confronting ecological change takes a collaborative leap with the NEON Ecological Forecasting Challenge - VT News

To ensure safe drinking water, experts forecast the health of lakes and reservoirs - VT News

Researchers co-locate to Steger Hall at the Fralin Life Sciences Institute to tackle infectious diseases and rapid environmental change - VT News

Using data to predict the future of ecosystems - VT News

Researchers receive NSF grant to create Ecological Forecasting Research Coordination Network - VT News

Testing the water: Virginia Tech team launches system to predict water conditions - VT News

Global Change Center researchers to forecast water quality with NSF support - VT News

Global Change Center researchers help water authority create ‘best water possible,’ prepare for warmer temperatures - VT News

Study forecasts growth rates of loblolly pine trees through first half of 21st century - VT News

Study incorporates ecological processes into Earth system models to aid climate change predictions - VT News

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Publications

Click for a full list of publications

Recent (2023 - )

Clark, C.M., J. Phelan, J. Ash, J. Buckley, J. Cajka, K. Horn, R.Q. Thomas, R. Sabo. 2023. Future climate change effects on U.S. forest composition may offset benefits of reduced atmospheric deposition of N and S. Global Change Biology 29:4793-4810 http://doi.org/10.1111/gcb.16817

Clark, C.M., R.Q. Thomas, and K.J. Horn. 2023. Above-ground tree carbon storage in response to nitrogen deposition in the U.S. is heterogeneous and may have weakened. Communications Earth & Environment 4: 35 https://doi.org/10.1038/s43247-023-00677-w

Dietze, M., R.Q. Thomas, J. Peters, C. Boettiger, A. Shiklomanov, and J. Ashander. A community convention for ecological forecasting: output files and metadata v1.0. Accepted at Ecosphere Pre-print: https://doi.org/10.32942/osf.io/9dgtq

Hounshell, A.G., B. M. D’Acunha, A. Breef-Pilz, M.S. Johnson, R. Quinn Thomas, C.C. Carey. 2023. Eddy covariance data reveal that a small freshwater reservoir emits a substantial amount of carbon dioxide and methane. Journal Geophysical Research - Biogeosciences 128: e2022JG007091 https://doi.org/10.1029/2022JG007091

Lofton, M.E., D.W. Howard, R.Q. Thomas, C. C Carey. 2023. Progress and opportunities in advancing near-term forecasting of freshwater quality. Global Change Biology 29: 1691-1714 https://doi.org/10.1111/gcb.16590

Smith, J.W., L.R. Johnson, and R.Q. Thomas. 2023. Assessing Ecosystem State Space Models: Identifiability and Estimation. Journal of Agriculture, Biological and Environmental Statistics 28: 442–465 https://doi.org/10.1007/s13253-023-00531-8

Smith, J.W., L.R. Johnson, and R.Q. Thomas. 2023. Parameterizing Lognormal state space models using moment matching. Environmental and Ecological Statistics 30: 385-419. https://doi.org/10.1007/s10651-023-00570-x

Thomas, R.Q., C. Boettiger, C.C. Carey, M.C. Dietze, L.R. Johnson, M.A. Kenney, J.S. Mclachlan, J.A. Peters, E.R. Sokol, J.F. Weltzin, A. Willson, W.M. Woelmer, and Challenge Contributors. 2023. The NEON Ecological Forecasting Challenge. Frontiers in Ecology and Environment 21: 112-113. https://doi.org/10.1002/fee.2616

Thomas, R.Q, R.P. McClure, T.N. Moore, W.M. Woelmer, C. Boettiger, R.J. Figueiredo, R.T. Hensley, C.C. Carey. Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S. Frontiers in Ecology and Environment 21: 220–226. https://doi.org/10.1002/fee.2623

Willson, A.M., H. Gallo, J.A. Peters, A. Abeyta, N. Bueno Watts, C.C. Carey, T.N. Moore, G. Smies, R.Q. Thomas, W.M. Woelmer, and J.S. McLachlan. 2023. Assessing opportunities and inequities in undergraduate ecological forecasting education. Ecology and Evolution 13: e10001. https://doi.org/10.1002/ece3.10001

Woelmer, W.M., T.N. Moore, M.E. Lofton, R.Q. Thomas, and C.C. Carey. 2023. Embedding communication concepts in forecasting training increases students’ understanding of ecological uncertainty Ecosphere 14: e4628 https://doi.org/10.1002/ecs2.4628

Wynne, J. H., W. M. Woelmer, T. N. Moore, R.Q. Thomas, K C. Weathers, and C. C. Carey. 2023. Uncertainty in projections of future lake thermal dynamics is differentially driven by global climate models and lake models. PeerJ 11:e15445 https://doi.org/10.7717/peerj.15445

Pre-prints undergoing peer-review

Olsson, F, T.N. Moore, C.C. Carey, A. Breef-Pilz, and R.Q. Thomas. 2023. A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts. ESS Open Archive https://doi.org/10.22541/essoar.169049097.75300247/v1

Wander, H.L., R.Q Thomas, T.N. Moore, M.E. Lofton, A. Breef-Pilz, C.C. Carey. Data availability affects forecast skill of 1 to 35-day water temperature forecasts in a eutrophic drinking water reservoir. ESS Open Archive https://doi.org/10.22541/essoar.168500255.59108131/v1

Wheeler, K., M. Dietze, D. LeBauer, J. Peters, A.D. Richardson, R.Q. Thomas, K. Zhu, U. Bhat, S. Munch, R.F Buzbee, M. Chen, B. Goldstein, J.S. Guo, D. Hao, C. Jones, M. Kelly-Fair, H. Liu, C. Malmborg, N. Neupane. D. Pal, A. Ross, V. Shirey, Y. Song, M. Steen, E.A. Vance, W.M. Woelmer, J. Wynne and L. Zachmann. Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge. SSRN http://dx.doi.org/10.2139/ssrn.4357147

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Bio

A.B. Dartmouth College, Hanover, NH (2001 - 2005)

M.S. University of New Hampshire, Durham, NH (2005 - 2007)

Ph.D. Cornell University, Ithaca, NY (2007 - 2012)

Post-doc, National Center for Atmospheric Research, Boulder, CO (2012 - 2013)

Assistant Professor, Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA (2013 - 2019)

Associate Professor, Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA (2019 - present)

Associate Professor, Department of Biological Sciences, Virginia Tech, Blacksburg, VA (2021 - present)

Visiting Scholar, Dartmouth College, Hanover, NH (2021 - 2022)

Visiting Scientist, Terrestrial Ecosystem Research Network, University of Queensland, Brisbane, QLD, Australia (2022 - present)

Data Science Faculty Fellow, College of Science, Virginia Tech, Blacksburg, VA (2022 - present)

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Team Members

Post-docs

Mary Lofton (Co-mentored with Cayelan Carey)
Maike Holthuijzen (Co-mentored with Cayelan Carey)
Freya Olsson (Co-mentored with Cayelan Carey)

Alumni

Joshua Rady (PhD)
Benjamin Ahlswede (PhD)
Michael Graham (PhD; Geospatial and Environmental Analysis; co-advised with Megan O’Rourke)
Kevin J. Horn (Post-doc)
Annika Jersild (MS)
Ryan McClure (Post-doc; Co-mentored with Cayelan Carey)
Wyatt McCurdy (MS)
Tadhg Moore (Post-doc; Co-mentored with Cayelan Carey)
Laura Puckett (UG)
John Smith (PhD in Statistics; co-advised with Leah Johnson)

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Teaching

FREC 3044: Environmental Data Science
FREC 5884: Ecological Forecasting
FREC 3604: Climate Science
FREC 5034: Ecosystem Dynamics
FREC 5204: Ecosystem and Climate

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Join Us

Interested Ph.D. or Masters of Science students should send an email letter of inquiry containing an overview of your research interests and your C.V. Please feel free to contact me with questions about the application process, graduate school at Virginia Tech, or potential research ideas.

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