Colorado mountains
From Long-Term Data to Understanding: Toward a Predictive Ecology
2015 LTER ASM Estes Park, CO - August 30 - September 2, 2015

Training graduate students in an era of ‘Big Ecology and Team Science’: The GLEON Fellowship Program

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Hilary Dugan
Paul Hanson
Kathie Weathers
Grace Hong


The Global Lake Ecological Observatory Network (GLEON) and the Long Term Ecological Research Network (LTER) were both initiated as a means of fostering collaboration across research sites as well as across sub-disciplines. In recent years, both networks have increasingly focused on environmental change. Both networks are active in data collection, data sharing, meta-analyses, and modeling, and promoting best practices and tools for these efforts. GLEON is a grassroots network, whereas the LTER network evolved from PI- and site-specific research questions. In this era of ‘big ecology and big data’, it is in both networks’ interest to promote inter-disciplinary collaborations, complex data synthesis, and scientific communication. Nonetheless, ‘traditional’ graduate programs provide little training for graduate students to operate successfully under this emerging paradigm.


As a response, and based on the success of the GLEON Graduate Student Association (GSA), the GLEON Fellowship program was initiated in 2013 to train graduate students in network science and macroscale ecology; and, importantly, in collaboration and leadership skills. Primary goals have been to expose graduate students from diverse training programs (e.g., distinct universities) to existing data networks and technologies as well as how to work (to practice team science) across cultures. As a small cohort, PhD students work together to develop technical, analytical, and team science skills, and complete publishable collaborative scientific projects over two years. Since its initiation, four separate research projects have been undertaken by GLEON Fellows, which draw on national/international datasets. The significant outcomes of the Fellowship are PhD students skilled in collaborative team science and dataset analyses, who are prepared to meet the future challenges and opportunities presented by network science. We believe this program design is extendable to LTER and other networks.