2018 ESA Annual Meeting (August 5 -- 10)

COS 82-6 - The all singing all dancing ecologist? Forming communities of practice by cross training graduate students in empirical and modelling approaches

Wednesday, August 8, 2018: 3:20 PM
245, New Orleans Ernest N. Morial Convention Center
David J.P. Moore, School of Natural Resources and Environment, University of Arizona, Tucson, AZ and Kim Novick, O'Neill School of Public and Environmental Affairs (SPEA), Indiana University, Bloomington, IN
Background/Question/Methods

During the last 20 years, a grass roots network of biometeorologists and ecologists has developed around the eddy covariance technology, which can quantify the exchange of carbon and water between the atmosphere to the biosphere. This is a complex technology requiring specialist training. We can now collect and store more observations of ecological systems than in the past. However, data collection in complex research fields has outstripped development of advanced computational analysis, data management, curation and synthesis. These skills in ecology fundamental to the development of sustainable and resilient systems their lack has created a bottleneck in research. Ecological sampling theory and the need to extend inference beyond a single site has led to new analytical techniques built upon open data sharing policies and collaboration between empirical scientists and those that use detailed statistical and process based modeling approaches. This drove a need to train students in a variety of analytical techniques, and to foster the development of collaborative networks that span career stages and expertise. To meet that need, since 2007, a group of volunteer researchers and educators have organized and executed a unique, early career summer course in flux measurement and advanced modeling; the "fluxcourse".

Results/Conclusions

The fluxcourse has trained more than 200 early career scientists in techniques that are fundamental to empirical measurement, data synthesis and modeling. After ten years of teaching this course we face new revolutions in data collection technologies, analytical tools as well as new research imperatives and ask how to train a new generation of ecologists to tackle these issues. This question is not limited to carbon and water cycling, but faces many ecological fields as regional, national and global ecological networks (LTER, ILTER, NEON, GLEON etc) move the emphasis from data collection to synthesis and ecological modeling and prediction. Here we offer to suggestions based on past experiences, successes and failures and ask for input from the ecological community in meeting these educational challenges.