Offered by: PaleoEcological Observatory Network (PalEON)
Dates: August 12-18, 2012
Course description: Estimating the impact of global change processes like land-use and climate on terrestrial ecosystems requires an integration of long-term data and ecosystem models. This course will provide 20 graduate students and postdocs with intensive training in the emerging tools that allow us to:
-estimate the signal and uncertainty in historical and paleoecological data -assimilate both signal and uncertainty into the current suite of terrestrial ecosystem models
The course has a hands-on, integrated curriculum emphasizing the data/model process from design through data collection, analysis and back to design. We will collect tree-rings, historical survey data and sedimentary data (e.g., pollen, charcoal, and macrofossils). Analysis of these data will take place in a Bayesian mode of inference addressing uncertainty in age-models, calibration of proxy data, and integration of diverse historical data. After an introduction to inference from ecosystem models in traditional "forward" mode, participants will integrate ecological parameters estimated from their data sets into these ecosystem models using formal Bayesian data assimilation.
Participating faculty: Mike Dietze (University of Illinois); Steve Jackson (University of Wyoming); Jason McLachlan (University of Notre Dame); Chris Paciorek (University of California Berkeley); Jack Williams (University of Wisconsin)
Location: University of Notre Dame Environmental Research Center, Land O'Lakes, WI, USA.
Fees: This workshop is funded by a grant from the National Science Foundation and is free to participants. You must provide your own means of transportation to Chicago, Illinois, or Madison, Wisconsin.
There are a limited number of travel grants available to applicants from NEON, Inc., member institutions (see www.neoninc.org/content/paleon-data-assimilation-course).
Application: We are seeking students with interests and backgrounds in paleoecology, terrestrial ecosystem modeling, and/or statistics. Send a CV, a statement detailing why you want to take the course and how you anticipate it helping your research, and arrange to have a letter sent from your major advisor supporting your application.
Apply to: Jason McLachlan at email@example.com
Deadline: March 30, 2012. Selections announced by April 15, 2012