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It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operatio...
Citations
... The latter (especially Level 3 in Fig. 2 ) would provide access to the rich environmental data NEON collects while enabling individual researchers the opportunity to carry out their own observational or experimental work (in the case of Level 4 in Fig. 2 ) on a study species that may not be focal to NEON's collections. Beyond collecting their own data, researchers could use existing colocated datasets ( Table S2 ; Nagy et al. 2021 ), which could further expand the scope of organismal biology possible via obser vator y networks. ...
Synopsis
Human activities are rapidly changing ecosystems around the world. These changes have widespread implications for the preservation of biodiversity, agricultural productivity, prevalence of zoonotic diseases, and sociopolitical conflict. To understand and improve the predictive capacity for these and other biological phenomena, some scientists are now relying on observatory networks, which are often composed of systems of sensors, teams of field researchers, and databases of abiotic and biotic measurements across multiple temporal and spatial scales. One well-known example is NEON, the US-based National Ecological Observatory Network. Although NEON and similar networks have informed studies of population, community, and ecosystem ecology for years, they have been minimally used by organismal biologists. NEON provides organismal biologists, in particular those interested in NEON's focal taxa, with an unprecedented opportunity to study phenomena such as range expansions, disease epidemics, invasive species colonization, macrophysiology, and other biological processes that fundamentally involve organismal variation. Here, we use NEON as an exemplar of the promise of observatory networks for understanding the causes and consequences of morphological, behavioral, molecular, and physiological variation among individual organisms.
... Offering access to comprehensive knowledge about HTS technologies, data analysis procedures, and their applications in managing plant diseases can democratize learning. These globally accessible resources support selfpaced learning and widespread knowledge dissemination (Nagy et al. 2021). ...
High-throughput sequencing (HTS) has instigated a paradigm shift in plant pathology, showcasing its transformative role in the management of plant diseases. As a powerful tool, HTS aids in identifying pathogens and enhances disease management strategies by detecting novel and emerging pathogens, tracking disease outbreaks, and contributing to developing disease-resistant cultivars. Despite these benefits, the implementation of HTS faces obstacles due to the complexity of data interpretation and economic factors that affect its widespread adoption. This comprehensive review summarizes the strengths, limitations, and opportunities associated with using HTS in managing plant diseases. The article also delves into the prospects of HTS, incorporating technological advancements, synergy with complementary methodologies, capacity-building initiatives, and the development of best practices and guidelines. By acknowledging and addressing these obstacles while harnessing the full capabilities of HTS, we advocate for a refined approach to managing plant diseases. This approach is critical for ensuring global food security, especially in the context of a growing global population and climate change.
... There are several ways for researchers to engage with the NEON data sets described in this paper. This ranges from simply downloading and using data from the NEON portal, to taking NEON data tutorials, contributing code resources to the NEON code hub, or proposing collaborations to conduct additional research (Nagy et al., 2021;SanClements et al., 2020). To fully utilize these large, integrated, publicly available data sets, techniques such as machine learning, deep learning, information theory, and others will be needed to uncover previously hidden connections between N cycling and ecological dynamics. ...
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial–temporal scales. Here, we introduce a continentally distributed, publicly available data set collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. First, we detail the sampling design and methods used to collect and analyze soil inorganic N pool and net flux rate data from 47 terrestrial sites. We address methodological challenges in generating a standardized data set, even for a network using uniform protocols. Then, we evaluate sources of variation within the sampling design and compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2). We observed wide spatiotemporal variation in inorganic N pool sizes and net transformation rates. Site explained the most variation in NEON’s stratified sampling design, followed by plots within sites. Organic horizons had larger pools and net N transformation rates than mineral horizons on a sample weight basis. The majority of sites showed some degree of seasonality in N dynamics, but overall these temporal patterns were not matched by CESM2, leading to poor correspondence between observed and modeled data. Looking forward, these data can reveal new insights into controls on soil N cycling, especially in the context of other environmental data sets provided by NEON, and should be leveraged to improve predictive modeling of the soil N cycle.
... NEON pairs publicly available data with a strong outreach and education effort to help realize this promise. In this way, NEON broadens access to macroecology by reducing barriers to entry, particularly cost, fieldwork requirements, and technical expertise (Nagy et al., 2021). An "instrument" such as NEON collecting standardized data at such scales leads to inevitable trade-offs-in the specific times, locations, and type of data that are sampled. ...
... UAS and NEON complement each other. Each can be a key tool for macroecology research, but their integration offers an opportunity to alleviate some of their fundamental constraints in a similar way as an integration of NEON with other Earth-observing networks Nagy et al., 2021). NEON data derive from "state-of-the-science" instrumentation with thorough documentation and are standardized at a continental scale. ...
... On the contrary, UAS operations are nimble and customizable, but the resulting data are relatively under-validated with data standards that are ad hoc, idiosyncratic, and lacking in consistency, which makes interoperability of those data across projects a challenge (Wyngaard et al., 2019). Realization of the benefits of UAS-NEON integration by ecologists is dually challenged by the relative novelty of these tools (Nagy et al., 2021;Wyngaard et al., 2019), as well as by a community gap in the data science skills needed to navigate their associated workflows Hampton et al., 2017;Nagy et al., 2021). Not knowing where to start with two new tools is a daunting proposition, and unstructured efforts to gain practical proficiency for research often come at the expense of doing research itself (Olah & Carter, 2017). ...
Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad‐extent, fine‐grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross‐scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad‐extent, fine‐grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up‐front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS‐using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high‐quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad‐extent, fine‐grain domain—more accessible via NEON/UAS integration.
... cience.org) that support research from local to global scales (Nagy et al., 2021). ...
1. Each year, the National Ecological Observatory Network's (NEON) Airborne Observation Platform (AOP) collects high‐resolution hyperspectral imagery, discrete and waveform lidar, and digital photography at a subset of 81 terrestrial and aquatic research sites throughout the United States. These open remote sensing data, together with NEON in situ sensor measurements and field observations, enable researchers to characterize ecological processes at multiple spatial and temporal scales.
2. Here we describe the sampling design for the AOP that aims to meet the diverse research needs of the ecological science community within the operational constraints affecting airborne data collection. Our spatial sampling protocol captures NEON instrumented systems, field plots and environmental gradients around each site while considering the context of airspace restrictions and remote sensing instrument capabilities. We use time series of moderate resolution imaging spectroradiometer (MODIS) satellite and PhenoCam near‐surface observations to define temporal sampling windows based on vegetation peak foliar greenness. We developed a probabilistic model based on MODIS reflectance imagery and Monte Carlo simulation to estimate sampling durations for cloud‐free data collection at each site.
3. Agreement in the estimated phenophase transition dates between MODIS Enhanced Vegetation Index and PhenoCam Green Chromatic Coordinate varied by vegetation class. Results from both sensors show that some vegetation classes have relatively consistent interannual peak greenness start‐ and end‐dates, while others experience high year‐to‐year variability in green‐up and senescence. In addition to phenological variability among sites, certain vegetation forms demonstrate distinct, asynchronous responses to climate, resulting in non‐overlapping peak greenness periods within a single site. Results from flight campaigns showed that the cloud‐likelihood model underestimated actual cloud conditions by 13%–26%, depending on the probability used.
4. Where interannual or intra‐site phenology is highly variable or clouds are a persistent problem, it becomes challenging to schedule domain deployments so that all sites are flown in cloud‐free conditions while their vegetation communities are in peak greenness. Despite limitations, application of cloud and peak greenness models to airborne sampling results in significant improvements to AOP data quality. Although most applicable to airborne sampling with hyperspectral and lidar instruments in piloted aircraft, these methods may be a valuable resource to deployment of Unmanned Aerial Vehicles for ecological research.
... The use of real-world examples and real ecological data allows students to relate to a sense of place, making the module content much more relevant to students [45]. Our teaching module adds to the growing number of teaching resources which are using NEON data [46], though it is the first, to the best of our knowledge, to use NEON data for teaching forecasting to undergraduates. Moreover, our module can be taught using different modalities (hybrid, virtual, in-person), which provides a flexible approach for integrating NEON data into ecology curricula [47]. ...
Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts.
... Methodological and epistemological challenges involved in using these data led the authors of this paper to recognize the necessity of having a team of collaborators to validate methods and test results before formally embedding them into a standard algorithmic process. While there is some research on the social and technical factors that allow for effective team science (Rhoten 2003, Oliver et al. 2018, there is room to consider how to best foster collaborations that can synthesize the wide variety of NEON data products to address interdisciplinary problems (e.g., Nagy et al. 2021). Interdisciplinary collaborations have been identified as avenues for fruitful and novel research in ecology and the environment as discussed above, but especially for understanding complex socioenvironmental issues (Palmer et al. 2016). ...
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.
Context
An increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers.
Objectives
The large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers.
Methods
We synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management.
Results
Eight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management.
Conclusions
Accessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management.
Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.
