NEON Workshop: Operationalizing Ecological Forecasts
Ecosystems are changing worldwide and critical decisions that affect ecosystem health and sustainability are being made every day. As ecologists, we have a responsibility to ensure that these decisions are made with access to the best available science.
However, to bring this idea into practice, ecology needs to make a substantial leap forward towards becoming a more predictive science. Furthermore, even for basic, conceptual questions there is a lot to be gained by addressing problems from a forecasting perspective, with more frequent data-model comparisons helping to highlight misunderstandings and reframe long-standing questions.
Ecological forecasting is occurring across a wide range of ecological sub-disciplines, but there is often insufficient communication and cross-fertilization of ideas and best practices. In particular, with the growth in environmental and ecological monitoring data, there is an enormous unmet potential to develop near real-time ecological forecasts - to use monitoring data not just respond to changing ecosystems, but to anticipate that change.
This workshop aims to advance this idea of "operational" ecological forecasting and will focus on identifying:
- In what ecological subdisciplines, and for what problems, would operational forecasts be most immediately useful?
- What are the low-hanging fruit for using NEON and other monitoring data to build operational forecasts?
- What are the hurdles to getting this done (theory, training, collaboration, technology, funding) and how might they be addressed?
Publication(s):
Dietze, M.C., Fox, A., Beck-Johnson, L.M., Betancourt, J.L., Hooten, M.B., Jarnevich, C.S., Keitt, T.H., Kenney, M.A., Laney, C.M., Larsen, L.G., Loescher, H.W., Lunch, C.K., Pijanowski, B.C., Randerson, J.T., Read, E.K., Tredennick, A.T., Vargas, R., Weathers, K.S., and White, E.P. (2018), Iterative near-term ecological forecasting: Needs, opportunities, and challenges. PNAS, 201710231; DOI: 10.1073/pnas.1710231115
Principal Investigator(s):
Michael Dietze (Boston University)
Participant(s):
Andrew M Fox (National Ecological Observatory Network)
Emily K Read (Office of Water Information)
Julio L Betancourt (Branch of Regional Research, Eastern Region)
Catherine S Jarnevich (Fort Collins Science Center)
Woody Turner (National Aeronautics and Space Administration)
Laurel Larsen (Branch of Regional Research, Eastern Region)
Yiqi Luo (University of Oklahoma)
Kathleen Weathers (Cary Institute of Ecosystem Studies)
Ethan White (University of Florida)
Jim Clark (Duke University)
James T Randerson (University of California, Irvine)
Melissa Kenney (University of Maryland)
Timothy Keitt (U.S. Geological Survey)
Bryan Pijanowski (Department of Forestry and Natural Resources, Purdue University)
Claire Lunch (National Ecological Observatory Network)
Christine Laney (National Ecological Observatory Network)
Mevin B Hooten (Colorado Cooperative Fish and Wildlife Research Unit)
N Thompson Hobbs (U.S. Geological Survey)
Andrew Tredennick (Utah State University)
Lindsay Beck-Johnson (Colorado State University)
- Source: USGS Sciencebase (id: 5637d7f8e4b0d6133fe72e85)
Ecosystems are changing worldwide and critical decisions that affect ecosystem health and sustainability are being made every day. As ecologists, we have a responsibility to ensure that these decisions are made with access to the best available science.
However, to bring this idea into practice, ecology needs to make a substantial leap forward towards becoming a more predictive science. Furthermore, even for basic, conceptual questions there is a lot to be gained by addressing problems from a forecasting perspective, with more frequent data-model comparisons helping to highlight misunderstandings and reframe long-standing questions.
Ecological forecasting is occurring across a wide range of ecological sub-disciplines, but there is often insufficient communication and cross-fertilization of ideas and best practices. In particular, with the growth in environmental and ecological monitoring data, there is an enormous unmet potential to develop near real-time ecological forecasts - to use monitoring data not just respond to changing ecosystems, but to anticipate that change.
This workshop aims to advance this idea of "operational" ecological forecasting and will focus on identifying:
- In what ecological subdisciplines, and for what problems, would operational forecasts be most immediately useful?
- What are the low-hanging fruit for using NEON and other monitoring data to build operational forecasts?
- What are the hurdles to getting this done (theory, training, collaboration, technology, funding) and how might they be addressed?
Publication(s):
Dietze, M.C., Fox, A., Beck-Johnson, L.M., Betancourt, J.L., Hooten, M.B., Jarnevich, C.S., Keitt, T.H., Kenney, M.A., Laney, C.M., Larsen, L.G., Loescher, H.W., Lunch, C.K., Pijanowski, B.C., Randerson, J.T., Read, E.K., Tredennick, A.T., Vargas, R., Weathers, K.S., and White, E.P. (2018), Iterative near-term ecological forecasting: Needs, opportunities, and challenges. PNAS, 201710231; DOI: 10.1073/pnas.1710231115
Principal Investigator(s):
Michael Dietze (Boston University)
Participant(s):
Andrew M Fox (National Ecological Observatory Network)
Emily K Read (Office of Water Information)
Julio L Betancourt (Branch of Regional Research, Eastern Region)
Catherine S Jarnevich (Fort Collins Science Center)
Woody Turner (National Aeronautics and Space Administration)
Laurel Larsen (Branch of Regional Research, Eastern Region)
Yiqi Luo (University of Oklahoma)
Kathleen Weathers (Cary Institute of Ecosystem Studies)
Ethan White (University of Florida)
Jim Clark (Duke University)
James T Randerson (University of California, Irvine)
Melissa Kenney (University of Maryland)
Timothy Keitt (U.S. Geological Survey)
Bryan Pijanowski (Department of Forestry and Natural Resources, Purdue University)
Claire Lunch (National Ecological Observatory Network)
Christine Laney (National Ecological Observatory Network)
Mevin B Hooten (Colorado Cooperative Fish and Wildlife Research Unit)
N Thompson Hobbs (U.S. Geological Survey)
Andrew Tredennick (Utah State University)
Lindsay Beck-Johnson (Colorado State University)
- Source: USGS Sciencebase (id: 5637d7f8e4b0d6133fe72e85)