This is a compilation of the RFI abstracts that address Research Designs, Experimental Design, and other responses received for the NSF NEON RFI Synthesis workshop at the USGS EROS Data Center in Sioux Falls, SD. See the call for RFI that solicited suggestions from the community in the Call for RFI document.
This document was released after the National Network Design meeting in Boulder, CO (February 2007). It outlines the strategy for integrating into the NEON design the relocatable themes recommended by the NSF EROS RFI Synthesis Workshop (February 2007). The themes are: land use, biodiversity-invasives-disease, and climate change-ecohydrology-biogeochemistry.
The goal of this call for a Request-for-Information (RFI) is to obtain information from the environmental science community to further refine the design and specifications of NEON. This information is needed to prepare the Project Execution Plan, a document required for the Preliminary Design Review, which is the second of three reviews that must occur prior to requesting funds from NSF to construct NEON. The responses to this call for RFIs can be viewed in the Abstracts for Non-site Based RFIs document
The NEON Design Consortium, consisting of 160 volunteer scientists, educators and engineers, played a critical role in determining the scientific and educational requirements that must be supported by NEON. The work of the Consortium resulted in the Integrated Science and Education Plan (ISEP). This document outlines the continental-scale stratification scheme employed in the NEON design.
Models are ubiquitous tools for advancing science; they allow scientists to deal with the complexity of the natural-human environment, with the interdisciplinarity of national environmental problems, and with the novelty and sheer quantity of data from observatories such as NEON. Models will play a central and essential role in NEON from the first steps of planning to the final stages of synthesis and forecasting.
Current approaches to studying the ecology and evolution of infectious disease systems have provided substantial theoretical development and data during the past decades. However, it is evident that in many cases, the rate at which we are generating substantial new knowledge is slowing. Our ability to understand higher level interactions that influence the patterns of diseases in the real world remain insufficient to allow us to predict outcomes and devise interventions that minimally impact the ecosystem.
