NEON is your observatory

The National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.

Soil microbial communities play critical roles in various ecosystem processes, but studies at a large spatial and temporal scale have been challenging due to the difficulty in finding the relevant samples in available data sets as well as the lack of standardization in sample collection and processing. The National Ecological Observatory Network (NEON) has been collecting soil microbial community data multiple times per year for 47 terrestrial sites in 20 eco‐climatic domains, producing one of the most extensive standardized sampling efforts for soil microbial biodiversity to date. Here, we introduce the neonMicrobe R package—a suite of downloading, preprocessing, data set assembly, and sensitivity analysis tools for NEON’s newly published 16S and ITS amplicon sequencing data products which characterize soil bacterial and fungal communities, respectively. neonMicrobe is designed to make these data more accessible to ecologists without assuming prior experience with bioinformatic pipelines. We describe quality control steps used to remove quality‐flagged samples, report on sensitivity analyses used to determine appropriate quality filtering parameters for the DADA2 workflow, and demonstrate the immediate usability of the output data by conducting standard analyses of soil microbial diversity. The sequence abundance tables produced by neonMicrobe can be linked to NEON’s other data products (e.g., soil physical and chemical properties, plant community composition) and soil subsamples archived in the NEON Biorepository. We provide recommendations for incorporating neonMicrobe into reproducible scientific workflows, discuss technical considerations for large‐scale amplicon sequence analysis, and outline future directions for NEON‐enabled microbial ecology. In particular, we believe that NEON marker gene sequence data will allow researchers to answer outstanding questions about the spatial and temporal dynamics of soil microbial communities while explicitly accounting for scale dependence. We expect that the data produced by NEON and the neonMicrobe R package will act as a valuable ecological baseline to inform and contextualize future experimental and modeling endeavors.

A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.

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 (data.neonscience.org). 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.

Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience.

Recent expansion in data sharing has created unprecedented opportunities to explore structure–function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data's potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30‐year time frame, including discrete‐return light detection and ranging (LiDAR) from an airborne observation platform. LiDAR is a well‐established and increasingly available remote sensing technology for measuring three‐dimensional characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 to 25+ pt/m2 depending on the instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the United States and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve of 16 structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7–8 pt/m2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and that users of these open‐source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.

Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.

Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.

ABSTRACTBelowground eukaryotic diversity serves a vital role in soil ecosystem functioning, yet the composition, structure, and macroecology of these communities are significantly under‐characterized. The National Ecological Observatory Network (NEON) provides publicly available datasets from long‐term surveillance of numerous taxa and ecosystem properties. However, this dataset is not routinely evaluated for its eukaryotic component, likely because analyzing metagenomes for eukaryotic sequences is hampered by low relative sequence abundance, large genomes, poorer eukaryote representation in public reference databases, and is not yet mainstream. We mined the NEON soil metagenome datasets for 18S rRNA sequences using a custom‐built pipeline and produced a preliminary assessment of biodiversity trends in North American soil eukaryotes. We extracted ~800 18S rRNA reads per sample (~22,000 reads per site) from 1455 samples from 495 plots across 45 NEON sites in 11 biomes, which corresponded to 5183 genera in 35 phyla. To our knowledge, this represents the first large‐scale soil eukaryote analysis of NEON data. We asked whether taxonomic richness paralleled patterns previously established ecological trends and found that eukaryotic richness was negatively correlated with pH, managed sites lowered eukaryotic richness by 47%, most biomes had a distinct eukaryotic community, and fire decreased eukaryotic richness. These findings parallel generally accepted ecological trends and support the notion that NEON soil metagenome datasets can and should be used to explore spatiotemporal patterns in soil eukaryote diversity, its association with ecosystem functioning, and its response to environmental changes in North America.

In 2013, the National Ecological Observatory Network (NEON) started collecting 30-year multi-faceted ecological data at various spatial and temporal scales across the US including ticks. Understanding the abundance and dynamics of disease vectors under changing environmental conditions in the long-term is important to societies, but sustained long-term collection efforts are sparse. Using hard-bodied tick data collected by NEON, the vegetation and atmospheric data and a statistical state-space model, which included a detection probability component, this study estimated the abundance of tick nymphs and adult ticks across a Florida NEON location. It took into account the spatial and temporal variation, and factors affecting detection. Its purpose was to test the applicability of data collected thus far and evaluate tick abundance. The study found an increase in tick abundance at this Florida location, and was able to explain spatial and temporal variability in abundance and detection. This approach shows the potential of NEON data. The NEON data collection is unique in scale, and promises to be of great value to understand tick and disease dynamics across the US. From a public health perspective, the detection probability of vectors can be interpreted as the probability of encountering that vector, making these types of analyses useful for estimating disease risk.

Expanded availability of estimates of ecosystem metabolism and gas exchange from the worlds streams and rivers is rapidly revising estimates of river contributions to global carbon budgets. Here, we present estimates of gross primary production, ecosystem respiration, and gas exchange from 27 streams and rivers across North America, including Puerto Rico, using data from the National Ecological Observatory Network (NEON). Further, we explore how aggregating and processing input data influences model outputs, expanding the methodological knowledge in approaching sensor collection and manipulation for ecosystem-scale modelling. We apply filters to input data to determine how different approaches to quality control of raw data influence the quantity and precision of estimates of ecosystem metabolism. Model estimates are high priority measures of ecosystem function that integrate additional NEON data products that will allow further understanding of stream and river biogeochemistry and ecosystem function across time and space.

Specific metrics for the status of US science education indicate that the country is losing its competitive edge on a global scale; among nations, the US ranks 22nd in density of broadband internet penetration and 72nd in density of mobile phone subscriptions, both of which are critical tools for science literacy and likely contribute to our international rank of 48th in quality of math and science education. Even worse, 69% of US public middle-school math students are taught by teachers without degrees or certificates in mathematics, and 93% of public middle-school teachers in physical sciences lack degrees in their subject (NAS 2010). All of these rankings represent indicators of global science leadership. In short, formal science education systems in the US lag behind those of many other countries, and our ability to train the next generation of scientists is at risk (Brewer and Smith 2010). In addition to shortfalls in public schools, US K–12 youth now watch at least 4 hours of television daily, and the average teenager spends a whopping 7.5 recreational hours daily in front of television and gaming screens (NAS 2010) – time that instead could be devoted to doing homework or experiencing the natural world through outdoor engagement. The formal STEM (science, technology, engineering, and mathematics) education metrics for US education are not keeping pace with those of other developed countries and are declining over time. Ecology education is experiencing one of the largest deficits: public schools have cut back on field trips and many K–12 students now experience nature virtually, rather than directly (Louv 2008; Lowman and Mourad 2010). Likewise, many undergraduate and graduate programs are eliminating their natural history programs, sometimes referred to as the "extinction of the ologies" (Lowman and Mourad 2010). In contrast, informal science education (ISE) is now providing a larger compensatory component of STEM education to students as well as to citizens and policy makers. Currently, US museums tally approximately 850 million visits annually, which is more than the attendance for all major league sporting events and theme parks combined (483 million in 2011; www.aam-us.org). Along with these visits, museums are also recording a burgeoning virtual visitorship, with an additional 524 million online visitors in 2006 alone, which has escalated during the past 5 years. In the US, there are over 17 000 museums, whose visitors range from several hundred for a rural museum with a local mission to >8 million annually for larger or national museums (www.aam-us.org); many of these ISE experiences bolster shortfalls in formal STEM education. Natural history museums, with their biodiversity collections and interpretive dioramas, serve as focal points to provide ISE about ecological concepts, including biodiversity, ecosystem services, climate change, and landscapes. In many cases, K–12 students become inspired by seeing, touching, and working with collections; and increasingly, virtual technologies and social media are becoming important drivers of science education concepts for youth outside of the formal classroom setting. Both undergraduate and graduate students can gain valuable training in science communication and outreach using the public floor of museums and other ISE platforms (Trautmann and Krasny 2006; Lowman and Mourad 2010). Given the declining competitiveness of US science education on a global scale, ISE has become an important pipeline for engaging the next generation of scientists and for contributing to otherwise declining ecological literacy. Of US adults surveyed nationally, <20% possess an "acceptable" level of scientific literacy and a majority have little knowledge of natural science, the process of research, or the results of contemporary scientific investigations, despite the importance of science to both human and ecological health (Field and Powell 2001; Miller 2004). Many ISE institutions are embracing the urgency of providing visitors with hands-on experiences to understand science and especially ecology; such efforts may translate to a healthy community through an understanding of fresh water, pollinators, clean air, agriculture, carbon sequestration, forest products, and myriad other ecosystem services. Educators often suggest "the best way to help the public understand the research process is to involve them directly through hands-on research" (Ucko 2004). Public Engagement with Science (PES) is a term used in ISE circles that emphasizes the growing importance of providing citizens with a better understanding of science as a driver of economics and health; ecology is often a conduit for science understanding, whereby museums foster a sense of wonder that leads to greater science literacy by getting kids "hooked" on science. But more analyses of PES in museum settings – including theoretically based assessments of activities – are sorely needed (Bonney et al. 2009). In my former home state of North Carolina, 39% of 8th graders report they "never or hardly ever" designed a science experiment, and another 29% report doing hands-on science activities only once or twice a month. Teaching methods that were traditional several decades ago – lectures, textbooks, recipes in laboratory sessions –continue to be the norm in science education despite national outcries calling for radical change (eg Brewer and Smith 2010). Soon, science teachers may face additional pressure to incorporate hands-on experiences into their courses. Like those of many other states, North Carolina's revised science standards call for inquiry-based learning experiences emphasizing the Next Generation Science standards. Historically restricted to museums as an outreach activity, ISE may infiltrate the public school classroom, so that "citizen-science" activities originating in museums can be virtually transmitted to classrooms for dynamic new science activities (eg Project GLOBE, Project BudBurst, and iNaturalist.org; see Trautmann et al. 2012; Zoellick et al. 2012; www.yourwildlife.org). Programs such as the National Ecological Observatory Network (NEON) have national mandates from Congress to implement citizen science (or some major and transformational capacity of ISE) to reach diverse stakeholders with data streams about North American ecosystems (Lowman et al. 2009). In addition, for states like North Carolina, with large numbers of practicing scientists, the mentoring of K–12 teachers by scientists has the potential to become another pathway for ISE (Lowman and Randle 2009). To keep North Carolina competitive in science (especially given the strength of Research Triangle Park, a huge driver of technology and jobs in the state), public–private partnerships funded a new wing of the North Carolina Museum of Natural Sciences that opened in April 2012. Called the Nature Research Center, it proposed to expand citizen science (ie PES) to diverse stakeholders using four innovative platforms of ISE (Dickinson and Bonney 2012). Drawing upon ecology and especially biodiversity as major drivers (since the museum houses a collection of over three million specimens of organisms endemic to the southeastern US), several ecology-based citizen-science programs were launched with marked success. These programs serve as a case study for integrating real nature and virtual technologies, thereby raising the ecological literacy of museum visitors. Researchers from the Friday Institute for Educational Innovation at North Carolina State University initiated visitor evaluations to track the success of the different platforms of informal learning that include: (1) citizen-science programs in partnership with local scientists (eg the Belly-Button Biodiversity program [Hulcr et al. 2012] or the "wild life of our homes" project [www.yourwildlife.org]); (2) virtual theater to connect K–12 students directly to environmental scientists working both in laboratories and in the field; (3) public education labs (called iLabs) that illustrate natural science techniques of "how we know what we know" about ecology and natural sciences; and (4) glass-walled laboratories where scientists work in the public eye and answer questions from onlookers about the scientific process. Will ISE platforms succeed in bolstering STEM education for students and visitors? With its myriad educational programs for diverse audiences, the California Academy of Sciences (CAS) is proving the success of ISE. Located in San Francisco, CAS's ecoliteracy programs are rooted in science learning, but take the next step to reconnect learners to the natural world and equip them with tools to foster healthy communities through environmental action (eg http://spotlight.macfound.org/featured-stories/entry/elizabeth-babcock). For example, the Academy's Teacher Institute on Science and Sustainability engages 3rd- to 5th-grade teachers in a cohort-based, 2-year training program to incorporate sustainability action in their science teaching. Using simple metaphors that make the science accessible, CAS's ocean action programs (hosted near a 200 000-gallon live coral-reef exhibit on the museum floor) acquaint visitors with the causes and mechanisms of ocean acidification, and its impact on coral reefs. Conservation-oriented stories, as well as action steps designed for individuals and families to reduce carbon emissions, are directly woven into the program's message. In addition, CAS has one of the museum world's first glass-walled laboratories, where visitors can "meet a scientist" and children can interact with diverse scientists as role models (Figure 1). Such innovative attempts to strengthen ISE may ultimately inspire the next generation of ecologists. Moreover, expansion of "virtual-plus-real" natural history educational platforms into other ISE institutions – botanical gardens, zoos, parks, and even school playgrounds (eg www.schoolofants.org) – will foster exciting opportunities to improve ecoliteracy, not just for students but also for their parents, for regional policy makers, and for a diverse public who enjoy destinations that offer ecology education in an informal setting. An informal science education (ISE) laboratory at the California Academy of Sciences encourages students and the general public to pose questions to practicing scientists. This interaction also allows visitors to learn how natural history hypotheses are tested by the scientific method. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Review for "Temporal design for aquatic organismal sampling across the National Ecological Observatory Network"

The National Ecological Observatory Network (NEON) intends to monitor mosquito populations across its broad geographical range of sites because of their prevalence in food webs, sensitivity to abiotic factors, and relevance for human health. We describe the design of mosquito population sampling in the context ofNEON's long‐term continental scale monitoring program, emphasizing the sampling design schedule, priorities, and collection methods. Freely availableNEONdata and associated field and laboratory samples, will increase our understanding of how mosquito abundance, demography, diversity, and phenology are responding to land use and climate change.

After introducing various aspects of the National Ecological Observatory Network (NEON) being planned and established in the US, the author tries to suggest the future direction for the development of similar activities of the NEON to be carried out in the region of East Asia including Korea. A review was carried out in terms of the challenges, major questions, missions, developmental history, and some other specifics of the NEON design. It was further extended to the discussion of the issues for the regional construction of the Ecological Observatory Network (EON) in East Asia. The author hopes that this review could be used as a preliminary guide in ultimately promoting and advancing research, science and technology in conservation and preservation of ecosystems being degraded due to anthropogenic disturbances in the region of East Asia.