The Global Lake Ecological Observatory Network (GLEON) Experiment: Socio-ecological advances and surprises
When we consider sensor networks, we often focus on hardware deployments and resulting data. Yet, for networks that cross institutional boundaries, such as distributed federations of observatories, people, sustainable collaborative models and community building are critical. They establish the linkages, enable access to data, and create scientific products. In the Global Lake Ecological Observatory Network (GLEON), we found that careful integration of three networks—people, hardware/data, and lakes—was essential to an effective research environment. Accomplishing integration is not trivial and requires a shared vision among members, explicit attention to the science of team science, and training of scientists across career stages. In GLEON these efforts have resulted in scientific inferences covering new scales, crossing broad ecosystem gradients, and capturing important environmental events. Network-level capital has been increased by deploying instrumented buoys, creating new data sets and publicly available models, analytical tools, and the formation of international teams of scientists. Our approach unites a diverse membership in GLEON-style team science, with emphasis on training and early career leadership.
Several socio-environmental surprises have resulted within and across our three networks, but especially GLEON’s network of people. They include the grassroots evolution of GLEON, and the emergence of the Collaborative Climate Committee, which arose out of a need to establish protocols and procedures that would ensure equal participation and engagement by GLEON participants. Our survey data also demonstrate that conscious efforts to increase diversity (e.g., gender, geographic, and career stage) within GLEON was successful. In addition, the establishment of the GLEON Student Association (GSA), representing 1/3 of the GLEON membership, has resulted in tangible, GSA-led products, evincing the GSA members’ leadership and power within the network. The surveys also indicate that early career scientists regard the establishment of new collaborations and networks, as well as GLEON working meetings, as ‘game changers’ in their science. While GLEON members continue to list data relevance as a main reason for not using network data, concerns about data reliability and validity declined from October 2008 to October 2009. Scientific understanding about lake function has expanded as a result of global data comparisons, e.g., demonstrating differential lake response to extreme events , comparative influence of abiotic drivers on lake physics; and surprising global patterns of lake metabolism. Finally, the development of open-source community models and our ability to integrate data with models is a major advance.