OPS 3

Advancing Ecological Understanding Using Network-Level Science

In an era of unprecedented impacts of human activities on ecosystems, ecologists are increasingly turning to big datasets from networks of long-term monitoring sites to help understand and predict how climate change, land use change and invasive species affect the world around us. These networks rely on methodologies that range from highly “top-down” standardized approaches, such as used by the National Ecological Observatory Network (NEON), through more emergent assemblies of sites employed by Principal Investigator driven science as represented by Long-Term Ecological Research (LTER) sites, Critical Zone Observatories (CZO) and AmerFlux, to “grass-root” coordination of experiments, such as the Nutrient Network (NutNet). The great power of such networks stems from the very large amounts of data they provide that sample ecosystem processes over a wide range of climatological and ecological conditions that can be used to develop general theory that can be employed to characterize ecosystem responses to environmental drivers at larger spatial and temporal scales. Information at these scales is most useful to decision makers and society-at-large. However, bringing together datasets from such diverse sources presents many challenges. Ecologists working with these large datasets must learn new and rapidly evolving technologies, and are reliant upon a level of support that is made possible only by the synergistic effect of collaboration. The primary goal of this session is to highlight on-going ecological research that provides insights into how to best enable scientific interoperability, including developing and sharing protocols, procedures, instrumentation, algorithms, metadata and models within- and across-networks. Fostering efficiencies in these interactions is essential if the full capabilities of these observation systems are to be leveraged to their maximum potential to produce innovative solutions to major challenges in ecology. Individuals are finding synergies,working together, and producing solutions to challenging problems across a range of topics such as comparability across diverse systems, developing new analytical and data processing, and spatial analysis methods. A secondary goal of this workshop is to explore how educators working at or with network-level science organizations make the large datasets meaningful to a variety of educational audiences. Given the enormity of the task, it is imperative that educators have an opportunity to share tools and approaches including the creation and dissemination of instructional modules and educational resoruces.