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Rapid changes in climate and land use and the resulting shifts in species distributions and ecosystem functions have motivated the development of the National Ecological Observatory Network (NEON). Integrating across spatial scales from ground sampling to remote sensing, NEON will provide data for users to address ecological responses to changes in climate, land use, and species invasion across the United States for at least 30 years. Although NEON remote sensing and tower sensor elements are relatively well known, the biological measurements are not. This manuscript describes NEON terrestrial sampling, which targets organisms across a range of generation and turnover times, and a hierarchy of measurable biological states. Measurements encompass species diversity, abundance, phenology, demography, infectious disease, ecohydrology, and biogeochemistry. The continental‐scale sampling requires collection of comparable and calibrated data using transparent methods. Data will be publicly available in a variety of formats and suitable for integration with other long‐term efforts. NEON will provide users with the data necessary to address large‐scale questions, challenge current ecological paradigms, and forecast ecological change.

Global change drivers influence ecological processes at multiple scales and manifest across most of Earth as changes in biodiversity, biogeochemical cycles, infectious disease incidence, and ecohydrology. Small-scale investigations provide compelling evidence of specific effects of global change on local systems, but are of limited use in modeling complex ecological processes at continental-to-global scales. Long-term observations distributed across a diversity of habitat types are needed to improve the ability to forecast ecological change at large spatial and temporal scales. This special issue introduces the Terrestrial Observation System ( TOS) of the National Ecological Observatory Network ( NEON), a long-term, continental-scale ecological research platform designed to deliver these large-scale datasets. The TOS measures biodiversity of key biota (soil microbes, insects, plants, small mammals), ecosystem productivity and biogeochemistry, infectious disease dynamics, phenology, and population dynamics. The articles in this special issue describe the scientific rationale for the sampling designs of the TOS, including an overview of protocols, locations, and frequencies of measurements. The science designs are a culmination of design requirements scoped by NEON and the National Science Foundation, best available practices put forth by the scientific community, input from technical working groups, and consideration of logistical and financial constraints by NEON staff. Within each site, measurements have been collocated to the extent possible to optimize linkages among different sampling elements. Integrated analyses of terrestrial observations with sensor-based, imagery, and remote-sensing data collected by other NEON subsystems can facilitate scaling of measured parameters to larger spatial and temporal scales. NEON is designed to collect data for 30 years, and make these data freely available on a public data portal (). Samples and specimens will be archived and available to the scientific community upon request. The open access approach to the Observatory will provide users with the resources necessary to map, understand, and predict the effects of global change drivers on ecological processes at a continental scale. [ABSTRACT FROM AUTHOR]

From 81 study sites across the United States, the US National Ecological Observatory Network (NEON), generates >75,000 samples per year. Samples range from soil and dust deposition material, tissue samples (e.g., small mammals and fish), DNA extracts, and whole organisms (e.g., ground beetles and ticks). Samples are collected, processed, and documented according to protocols that are standardized across study sites and according to the needs of the ecological research community for future studies. NEON has faced numerous challenges with managing data related to these many diverse physical samples, particularly when data are gathered at numerous steps throughout processing. Here, we share these challenges as well as solutions, including innovative semantically driven software tools and processing pipelines that manage data from each sample's point of collection to its ultimate fate (consumption, archive facility, or partnering data repository) while maintaining links across sample hierarchies. [ABSTRACT FROM AUTHOR]