Introduction to the sampling designs of the <scp>N</scp>ational <scp>E</scp>cological <scp>O</scp>bservatory <scp>N</scp>etwork <scp>T</scp>errestrial <scp>O</scp>bservation <scp>S</scp>ystem

Thorpe, Andrea S.; Barnett, David T.; Elmendorf, Sarah C.; Hinckley, Eve‐Lyn S.; Hoekman, David; Jones, Katherine D.; LeVan, Katherine E.; Meier, Courtney L.; Stanish, Lee F.; Thibault, Katherine M.

doi:10.1002/ecs2.1627

Cited by 73 publications

(65 citation statements)

References 13 publications

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“…The specific sample size is ultimately determined in the science design associated with each TOS measurement (Thorpe et al. ); trend detection, dependent on determination of space‐time covariance structures (Cressie and Wickle ), will depend on the diversity of analytical approaches applied to the data. Given the variety of research approaches and questions to be addressed with NEON data products, the sample design framework must be applicable to classical, contemporary, and future statistical approaches that characterize patterns in space and through time (Cressie et al.…”

Section: Design Criteriamentioning

confidence: 99%

“…At the scale of NEON sites, the sampling frame depends on the type of plot (Thorpe et al. ) and taxonomic group of interest. In the case of many of the vegetation and soil observations (Thorpe et al.…”

Section: Spatial Sampling Design For the Terrestrial Observation Systemmentioning

confidence: 99%

“…In the case of many of the vegetation and soil observations (Thorpe et al. ), the frame typically corresponds to an associated management type or ownership boundary (Fig. ).…”

Section: Spatial Sampling Design For the Terrestrial Observation Systemmentioning

confidence: 99%

“…This typically includes the location of the tower‐based sensor measurements and the aquatic measurements at some sites (Thorpe et al. ). Design constraints limit the spatial extent of some observations.…”

Section: Spatial Sampling Design For the Terrestrial Observation Systemmentioning

confidence: 99%

“…, Thorpe et al. ) in a way that informs the continental‐scale Observatory presents a formidable challenge. The strategy is described herein: Guided by NEON principles and requirements, the TOS sampling design provides a data collection framework that is statistically rigorous, operationally efficient, flexible, and readily facilitates integration with other data to advance the understanding of the drivers of and responses to ecological change.…”

Section: Introductionmentioning

confidence: 99%

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

Barnett

Duffy²,

Schimel

et al. 2019

Ecosphere

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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.

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Section: Design Criteriamentioning

confidence: 99%

Section: Spatial Sampling Design For the Terrestrial Observation Systemmentioning

confidence: 99%

“…In the case of many of the vegetation and soil observations (Thorpe et al. ), the frame typically corresponds to an associated management type or ownership boundary (Fig. ).…”

Section: Spatial Sampling Design For the Terrestrial Observation Systemmentioning

confidence: 99%

Section: Spatial Sampling Design For the Terrestrial Observation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Barnett

Duffy²,

Schimel

et al. 2019

Ecosphere

Self Cite

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Forest Canopy Structural Complexity and Light Absorption Relationships at the Subcontinental Scale

et al. 2018

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Understanding how the physical structure of forest canopies influences light absorption is a long‐standing area of inquiry fundamental to the physical sciences, including the modeling and interpretation of biogeochemical cycles. Conventional measures of forest canopy structure used to infer canopy light absorption are often limited to leaf or vegetation area indexes. However, LiDAR‐derived measures of canopy structural complexity (CSC) that describe the arrangement of vegetation may improve prediction of canopy light absorption by providing novel information on canopy‐light interactions not regulated by leaf area alone. We measured multiple indexes of CSC, vegetation area index (VAI), and the fraction of photosynthetically active radiation absorbed (fPAR) across the eastern United States using portable canopy LiDAR to evaluate how different canopy structural attributes relate to fPAR. Our survey included sites from the National Ecological Observation Network and university field stations. Measures of CSC were more strongly coupled with fPAR under high light (>1,000 μmol m−2 s−1 PAR). Under low light conditions, when diffuse light predominates, light scattering weakens the dependency of fPAR on CSC. A multivariate model including CSC parameters and VAI explains ~89% of the intersite variance in fPAR, an improvement of over a VAI only linear model (r2 = 0.73). The inclusion of CSC metrics in canopy light absorption models could increase confidence in predictions of biogeochemical cycles and energy balance.

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Ecological prediction at macroscales using big data: Does sampling design matter?

Soranno

Cheruvelil

Liu

et al. 2020

Ecological Applications

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Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8‐million‐km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non‐random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

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Introduction to the sampling designs of the National Ecological Observatory Network Terrestrial Observation System

Cited by 73 publications

References 13 publications

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

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

Forest Canopy Structural Complexity and Light Absorption Relationships at the Subcontinental Scale

Ecological prediction at macroscales using big data: Does sampling design matter?

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