The terrestrial organism and biogeochemistry spatial sampling design for the National Ecological Observatory Network

Abstract

The National Ecological Observatory Network (NEON) seeks to facilitate ecological prediction at a continental scale by measuring processes that drive change and responses at sites across the United States for thirty years. The spatial distribution of observations of terrestrial organisms and soil within NEON sites is determined according to a “design‐based” sample design that relies on the randomization of sampling locations. Development of the sample design was guided by high‐level NEON objectives and the multitude of data products that will be subjected to numerous analytical approaches to address the causes and consequences of ecological change. A requirement framework permeates the NEON design, ensuring traceability from each facet of the design to the high‐level requirements that make the NEON mission statement actionable. Requirements were developed for the terrestrial sample design to guide the key components of the design: Randomizing the sample locations ensures the unbiased collection of data, is appropriate for organisms and soil, and provides data suitable for a variety of analyses. Stratification increases efficiency and allows sampling to focus on those parts of the landscape measured by other NEON observation platforms. Attention to the sample size and spatial plot allocation ensures that data products will be sufficient to inform questions asked of the data and the NEON objectives. Establishing a framework with the capacity for re‐evaluate and design iteration allows for adaption to unexpected challenges and optimization of the sample design based on early data returns. The utility of the NEON sampling design is highlighted by its application across terrestrial systems. The data generated from this unique design will be used to quantify patterns in: the abundance and diversity of small mammals, breeding birds, insects, and soil microbes; vegetation structure, biomass, productivity, and diversity; and soil biogeochemistry.

Representing Organic Matter Thermodynamics in Biogeochemical Reactions via Substrate-Explicit Modeling

“…As an initial effort, WHONDRS provides the collection of high-resolution OM profiles such as FTICR-MS data across rivers in the world (Stegen et al, 2018a;Chu et al, 2019;Danczak et al, 2019;Garayburu-Caruso et al, 2019;Goldman et al, 2019;Renteria et al, 2019;Stegen et al, 2019;Wells et al, 2019;Danczak et al, 2020). Other networked efforts, such as the National Ecological Observation Network (Teeri and Raven, 2002;Barnett et al, 2019), provides similar kinds of data that are amenable for analysis vis SXM. Analysis of these data, which are collected and analyzed consistently across systems, using the SXM framework will significantly improve our understanding of the level of heterogeneity across space and time in OM consumption and respiration, and thus could be used as a critical tool for more mechanistic predictions of spatial and temporal variation in stream/river CO2 emissions and other coupled biogeochemical rates from local to global scales.…”

The plant diversity sampling design for The National Ecological Observatory Network

“…). Data to be collected: Plant tissue from a subset of species found at each site will be collected and made available for genetic analyses, plant species presence and abundance will be recorded in multi‐scale vegetation plots, and functional traits will be assessed using a variety of protocols. Population to be sampled: The target will be the species in all but the most rare cover types (>5% of the site) within the extent of NEON sites. A statistically rigorous sample design provides a framework for sampling (Barnett et al., in press). Sampling frame: The spatial extent of NEON sites bounds the area available to sample plant species (Bonar et al.…”

“…The unbiased sample associated with randomization (Cochran , Thompson ) is the foundation of the NEON sample design (Barnett et al., in press). It eliminates the potential for bias and allows design‐based inference of population parameters from points to the unsampled landscape with design‐based estimators (Sarndal , Stehman ).…”

“…Additional power analysis (Thompson ) described sample sizes necessary for differentiating magnitude of trends between two sites as an initial case study. The test prescribed a sample size (about 20 plots/site depending on variability in space and time) robust to a variety of questions that might be asked of the data (see Barnett et al., in press). The obligation to insure the data provide tangible contributions to elucidating the drivers of space–time trends (Legg and Nagy , Keller et al.…”

“…Various modeling approaches might benefit from the collection of data according to an alternative stratification or gradient or a random sample, but most can also ingest data based on principles of randomization. The design‐based estimators associated with the NEON design were developed but are discussed elsewhere (Barnett et al., in press).…”