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Workshop: “Error propagation for carbon estimation" December 4-6, 2013, Mexico City. 

Yanai, R.D. 2013. Methods of Uncertainty Propogation in Estimating Forest Biomass. Session 4: Uncertainty Combination, Workshop on "Error propagation for carbon estimation, December 4-6, 2013, Camino Real Aeropuerto Hotel, Mexico City. The workshop was organized by the Mexican National Forestry Commission through the Reinforcing REDD+ and South-South cooperation Project, with the suppor of the Government of Norway, Food And Agriculture Organization of the United Nations (FAO), and the United Nations Development Programme (UNDP). For more information about workshop objectives and sessions, or to peruse the program or view PDFs of each presenter's slides, please visit: English | en español

Uncertainty Analysis: A Critical Step in Ecological Synthesis @ ESA, August 5, 2013

Ruth Yanai, Jeffrey Taylor and Mark Harmon organized, and John Battles moderated an "Organized Oral Session" on Uncertainty Analysis: A Critical Step in Ecological Synthesis at the 2013 Ecological Society of America Annual Meeting in Minneapolis, MN.   


Ecology is entering an exciting era in which the number and availability of long-term data sets are increasing exponentially. There is an unprecedented need to synthesize these data to address current scientific and societal problems. Great progress has been made on linking data and theory, including spatial integration and interdisciplinary combination. The question is no longer how to synthesize, but how well we link information from disparate sources and how to identify the most important areas for improvement. These synthetic approaches will demand increased proficiency and rigor in uncertainty analysis, to provide a metric of progress in synthesis science. This OOS highlights current developments in uncertainty estimation across many fields of ecology and provided guidance for large-scale synthesis research. Speakers were encouraged to provide recommendations for standardized approaches to uncertainty estimation and a vision for meeting future needs. Further development, understanding, and dissemination of the latest statistical techniques for deriving these estimates both inform ecological sampling design and equip up-and-coming ecologists with critical skills. Speakers examined sources of uncertainty and its general role in synthesis science. Case studies included a range of topics and approaches ranging from population ecology and small watershed nutrient cycling budgets to landscape carbon budgets. Methodologies presented include parametric statistical approaches, bootstrap analysis, Monte Carlo sampling, and Bayesian hierarchical analysis. Uncertainty introduced by spatial and temporal interpolation are common themes across scales from plots to the continental ecological observatory network.

Individual Presentations:

  • Uncertainty analysis: An evaluation metric for synthesis science.  Mark E. Harmon*, Oregon State University Abstract | Slideshare
  • Quantifying uncertainty in ecology: Examples from small watershed studies.  John L. Campbell*, United States Department of Agriculture Forest Service; Ruth D. Yanai, SUNY College of Environmental Science and Forestry; Mark B. Green, Plymouth State University Abstract | Slideshare
  • Better ignorant than misled: Including uncertainty in forecasts supporting management and policy.  N. Thompson Hobbs*, Colorado State University Abstract | PDF | Slideshare
  • Global Sensitivity Analysis for Impact Assessments.  Matthew Aiello-Lammens*, Stony Brook University; H. Resit Akcakaya, Stony Brook University.  Abstract | Slideshare
  • Optimizing environmental monitoring designs.  Carrie R. Levine, UC Berkeley; Ruth D. Yanai*, SUNY College of Environmental Science and Forestry; Gregory Lampman, NYSERDA; Douglas A. Burns, US Geologic Survey; Charles T. Driscoll, Syracuse University; Gregory B. Lawrence, U.S. Geological Survey; Jason A. Lynch, US Environmental Protection Agency; Nina Schoch, Biodiversity Research Institute. Abstract | Slideshare
  • Uncertainty due to gap-filling in long-term hydrologic datasets.  Craig R. See*, SUNY College of Environmental Science and Forestry; Ruth D. Yanai, SUNY College of Environmental Science and Forestry; Mark B. Green, Plymouth State University; Douglas I. Moore, University of New Mexico. Abstract | PDF
  • Uncertainty in an uncertain world: Using scientific judgment for evaluating uncertainty in measurement results.  Janae L. Csavina*, National Ecological Observatory Network (NEON, Inc.); Jeffrey Taylor, National Ecological Observatory Network (NEON, Inc.); Joshua A. Roberti, National Ecological Observatory Network (NEON, Inc.) Abstract | Slideshare (no audio)
  • NEON's approach to uncertainty estimation for sensor-based measurements.  Joshua A. Roberti*, National Ecological Observatory Network (NEON, Inc.); Jeffrey R. Taylor, National Ecological Observatory Network (NEON, Inc.); Henry W. Loescher, National Ecological Observatory Network (NEON, Inc.); Janae L. Csavina, National Ecological Observatory Network (NEON, Inc.); Derek E. Smith, National Ecological Observatory Network (NEON, Inc.) Abstract | Slideshare
  • Estimating uncertainty for continental scale measurements.  Jeffrey Taylor*, National Ecological Observatory Network (NEON, Inc.); Joshua Roberti, NEON, Inc.; Derek Smith, National Ecological Observatory Network (NEON, Inc.); Steve Berukoff, National Ecological Observatory Network; Henry W. Loescher, National Ecological Observatory Network (NEON, Inc.)  Abstract | Slideshare
  • Reducing uncertainty through data-driven model development.  David S. LeBauer*, University of Illinois; Michael Dietze, Boston University; Deepak Jaiswal, University of Illinois; Rob Kooper, University of Illinois; Stephen P. Long, University of Illinois at Urbana-Champaign; Shawn P. Serbin, University of Wisconsin - Madison; Dan Wang, University of Illinois. Abstract | Slideshare

Excerpts from the February 8 2011 Webinar

History of QUEST-Ruth Yanai explains what we do, why it's important, and how it came to be.

Sources of Uncertainty-Mark Harmon sums up where present knowledge is lacking

Uncertainty Analysis of Hubbard Brook hydrologic fluxes-Mark Green explains the use of Theissen polygons and of Monte Carlo and the Annual dissolved inorganic nitrogen stream export whole Webinar (~2 h mwv)

March 2011

The first QUEST meeting was held in Boston on March 14-15 2011.

We made a lot of progress on developing plans for analyses to inform future papers, presentations, and posters at a variety of meetings in the coming year.

You can access the meeting agenda; notes are password protected.

Quantifying Uncertainty in Ecological Studies, August 2011

7 August 2011
ESA Annual Meeting, Austin, Tx

Abstract: Ecological studies relying on complex calculations, such as ecosystem budgets, sometimes report results without any indication of uncertainty, which makes it difficult to evaluate the significance of findings or make comparisons across systems. We present examples of a Monte Carlo approach to estimating uncertainty in key ecological fluxes and pools such as inputs in precipitation, outputs in streamflow, and stores in vegetation. We have examples of Monte Carlo analyses for N at the Hubbard Brook Experimental Forest in New Hampshire, which have been implemented in Excel, R, and SAS. Participants should bring laptop computers and ecological data and calculations in need of uncertainty analysis (you can use ours if you don't have your own). You will need to know (or guess) the uncertainty in all the parameters used in the calculations. At the end of the workshop, some participants will have documented the uncertainty in their result. All participants will understand the principles of Monte Carlo sampling and will have tools for implementing uncertainty analyses.