Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

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 operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Key Edaphic Properties Largely Explain Temporal and Geographic Variation in Soil Microbial Communities across Four Biomes

<div><p>Soil microbial communities play a critical role in nutrient transformation and storage in all ecosystems. Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes. We examined spatial and seasonal variation in microbial communities based on 16S rRNA gene sequencing and phospholipid fatty acid (PLFA) composition across four biomes: a tropical broadleaf forest (Hawaii), taiga (Alaska), semiarid grassland-shrubland (Utah), and a subtropical coniferous forest (Florida). In this study, we used a team-based instructional approach leveraging the iPlant Collaborative to examine publicly available National Ecological Observatory Network (NEON) 16S gene and PLFA measurements that quantify microbial diversity, composition, and growth. Both profiling techniques revealed that microbial communities grouped strongly by ecosystem and were predominately influenced by three edaphic factors: pH, soil water content, and cation exchange capacity. Temporal variability of microbial communities differed by profiling technique; 16S-based community measurements showed significant temporal variability only in the subtropical coniferous forest communities, specifically through changes within subgroups of <i>Acidobacteria</i>. Conversely, PLFA-based community measurements showed seasonal shifts in taiga and tropical broadleaf forest systems. These differences may be due to the premise that 16S-based measurements are predominantly influenced by large shifts in the abiotic soil environment, while PLFA-based analyses reflect the metabolically active fraction of the microbial community, which is more sensitive to local disturbances and biotic interactions. To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature. Free, open access datasets and data sharing platforms are powerful tools for integrating research and teaching in undergraduate and graduate student classrooms. They are a valuable resource for fostering interdisciplinary collaborations, testing ecological theory, model development and validation, and generating novel hypotheses. Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.</p></div

Microbial community variation across sites at all time points using NMDS ordination.

<p>A) Variation in 16S rRNA based communities was only significant by site (F = 69.003, p = 0.001) and not by time (F = 1.003, p = 0.332). The environmental variables that explained significant variation in 16S-based communities were pH, SWC, CEC, Na⁺, Mg⁺ and Ca²⁺. B) Variation in lipid-based communities was significant by site (F = 38.964, p = 0.001) and over time (F = 7.381, p = 0.001). The environmental variables that explained significant variation in the lipid-based communities were SWC, pH, CEC, OM, Ca²⁺, Na⁺, K⁺, C:N, TN and Mg⁺.</p

Factors explaining the variation in 16S-based and lipid-based communities across seasons at four ecosystem sites, as described by PERMANOVA analysis.

<p>NS indicates not significant.</p

ANOVA and pairwise Tukey analysis to determine differences between sites and sample dates, based on PLFA and 16S analysis all dates and all sites (corresponds to Fig 3).

<p>Superscript letter values indicate statistical differences between clusters of communities associated with MDS1 (x-axis) and MDS2 (y-axis). SD indicates standard deviation.</p><p>ANOVA and pairwise Tukey analysis to determine differences between sites and sample dates, based on PLFA and 16S analysis all dates and all sites (corresponds to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135352#pone.0135352.g003" target="_blank">Fig 3</a>).</p

Average values for all measured (A) soil environmental variables, (B) dominant 16S rRNA-determined bacterial phyla, and (C) grouped lipids ± 95% confidence intervals for all soil samples at all time points.

<p>Columns in gray indicate time points closest to peak greenness at each site, which are used for cross-site comparisons. Significant differences over time, within sites, are indicated with * (p < 0.05), ** (p < 0.01), *** (p < 0.001), as compared to the time point at peak greenness using repeated measures ANOVA. Differences between sites are not indicated here, but are described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135352#pone.0135352.g002" target="_blank">Fig 2A</a>.</p

ANOVA and pairwise Tukey’s analysis to determine differences between sites at peak greenness, based on PLFA and 16S analysis (corresponds to Fig 2).

<p>Superscript letter values indicate statistical differences between clusters of communities associated with MDS1 (x-axis) and MDS2 (y-axis). SD indicates standard deviation.</p><p>ANOVA and pairwise Tukey’s analysis to determine differences between sites at peak greenness, based on PLFA and 16S analysis (corresponds to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135352#pone.0135352.g002" target="_blank">Fig 2</a>).</p

Changes in 16S rRNA-based community composition in Florida soils over time.

<p>(A) Relative abundances of all taxa classified at the phylum level and unclassified taxa over time. (B) NMDS ordination of all order-level taxa classified within the phylum <i>Proteobacteria</i>, which did not vary over time (ANOSIM R = 0.03968, p = 0.148). (C) NMDS ordination of all order-level taxa classified within the phylum <i>Actinobacteria</i>, which did not vary over time (ANOSIM R = 0.04861, p = 0.171). (D) NMDS ordination of all order level taxa classified within the phylum <i>Acidobacteria</i>, which varied significantly over time (ANOSIM R = 0.2057, p = 0.003).</p