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\nBIO Distinguished Lecture Series – Michael Dietze\n

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\n\nPublisher:\n\nScience360\n\n\n
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\nBIO Distinguished Lecture Series – Michael Dietze: \"Solving the challenge of predicting nature: How close are we and how do we get there?\"Is nature predictable? If so, how can we better manage and conserve ecosystems? Near-term...\nBIO Distinguished Lecture Series – Michael Dietze:

\"Solving the challenge of predicting nature: How close are we and how do we get there?\"

Is nature predictable? If so, how can we better manage and conserve ecosystems? Near-term ecological forecasting is an emerging interdisciplinary research area that aims to improve researchers' ability to predict ecological processes on timescales that can be validated and updated. On September 27, 2018, the National Science Foundation's (NSF) Directorate for Biological Sciences invited media and members of the public to a Distinguished Lecture by Dr. Michael Dietze of Boston University. An ecologist who leads the university's Ecological Forecasting Laboratory, Dr. Dietze discussed the challenges and opportunities in near-term ecological forecasting, which span advances in environmental monitoring, statistics and cyberinfrastructure.

Dr. Dietze is interested in ways that iterative forecasts can improve and accelerate basic environmental science while making that science more directly relevant to society. Dietze's plant ecology lab led the development of PEcAn, the Predictive Ecosystems Analyzer, an open-source software platform for analyzing diverse data and ecosystem models, funded by grants from NSF, NASA and others. In his Distinguished Lecture, Dr. Dietze presents a framework to understand the predictability of a wide range of ecological processes and highlighted ongoing efforts to build an ecological forecasting community of practice.\n
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Resource Details

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Grade
12th - Higher Ed
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Subjects
Science
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Instructional Videos
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Date
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2018
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Audiences
For Teacher Use
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scientists, case studies, weather forecasting
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ecological processes, mechanistic understanding, national science foundation, relative importance, physical sciences, process error, comparative approach, ecological forecasting, scientific method, decision support, external drivers, carbon cycle, learning, current state, interesting question, science, late 50s, make predictions, decision scientists, general science, iterative forecasting, internal dynamics, informatics problem, experiments, wide range, error partitioning, steady improvement, initial conditions, science experiments, sciences, cool app, forecasting problem, long dead, real world, develop standards, ecological forecasts, weather forecasts, soil microbiome, iteratively updating, cyber infrastructure, term forecasts, computer scientists, ecological problems
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