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

Author

R. Chelsea Nagy, Jennifer K. Balch, Erin K. Bissell, Megan E. Cattau, Nancy F. Glenn, Benjamin S. Halpern, Nayani Ilangakoon, Brian Johnson, Maxwell B. Joseph, Sergio Marconi, Catherine O’Riordan, James Sanovia, Tyson L. Swetnam, William R. Travis, Leah A. Wasser, Elizabeth Woolner, Phoebe Zarnetske, Mujahid Abdulrahim, John Adler, Grenville Barnes, Kristina J. Bartowitz, Rachael E. Blake, Sara P. Bombaci, Julien Brun, Jacob D. Buchanan, K. Dana Chadwick, Melissa S. Chapman, Steven S. Chong, Y. Anny Chung, Jessica R. Corman, Jannelle Couret, Erika Crispo, Thomas G. Doak, Alison Donnelly, Katharyn A. Duffy, Kelly H. Dunning, Sandra M. Duran, Jennifer W. Edmonds, Dawson E. Fairbanks, Andrew J. Felton, Christopher R. Florian, Daniel Gann, Martha Gebhardt, Nathan S. Gill, Wendy K. Gram, Jessica S. Guo, Brian J. Harvey, Katherine R. Hayes, Matthew R. Helmus, Robert T. Hensley, Kelly L. Hondula, Tao Huang, Wiley J. Hundertmark, Virginia Iglesias, Pierre‐Andre Jacinthe, Lara S. Jansen, Marta A. Jarzyna, Tiona M. Johnson, Katherine D. Jones, Megan A. Jones, Michael G. Just, Youssef O. Kaddoura, Aurora K. Kagawa‐Vivani, Aleya Kaushik, Adrienne B. Keller, Katelyn B. S. King, Justin Kitzes, Michael J. Koontz, Paige V. Kouba, Wai‐Yin Kwan, Jalene M. LaMontagne, Elizabeth A. LaRue, Daijiang Li, Bonan Li, Yang Lin, Daniel Liptzin, William Alex Long, Adam L. Mahood, Samuel S. Malloy, Sparkle L. Malone, Joseph M. McGlinchy, Courtney L. Meier, Brett A. Melbourne, Nathan Mietkiewicz, Jeffery T. Morisette, Moussa Moustapha, Chance Muscarella, John Musinsky, Ranjan Muthukrishnan, Kusum Naithani, Merrie Neely, Kari Norman, Stephanie M. Parker, Mariana Perez Rocha, Laís Petri, Colette A. Ramey, Sydne Record, Matthew W. Rossi, Michael SanClements, Victoria M. Scholl, Anna K. Schweiger, Bijan Seyednasrollah, Debjani Sihi, Kathleen R. Smith, Eric R. Sokol, Sarah A. Spaulding, Anna I. Spiers, Lise A. St. Denis, Anika P. Staccone, Kaitlin Stack Whitney, Diane M. Stanitski, Eva Stricker, Thilina D. Surasinghe, Sarah K. Thomsen, Patrisse M. Vasek, Li Xiaolu, Di Yang, Rong Yu, Kelsey M. Yule, and Kai Zhu

Subjects
community, continental‐scale ecology, diversity, inclusion, National Ecological Observatory Network, open data, Ecology, QH540-549.5
Abstract

Abstract 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.

Published
2021
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3. A spatially explicit, empirical estimate of tree-based biological nitrogen fixation in forests of the United States

Author

Duncan N. L. Menge, Wenying Liao, Steven S. Perakis, Anika P. Staccone, Jana E. Compton, and Christopher M. Clark

Subjects
0106 biological sciences, Atmospheric Science, Global and Planetary Change, Nutrient cycle, Forest inventory, 010504 meteorology & atmospheric sciences, biology, 010604 marine biology & hydrobiology, Robinia, Temperate forest, biology.organism_classification, 01 natural sciences, Article, Agronomy, Abundance (ecology), Nitrogen fixation, Environmental Chemistry, Ecosystem, Spatial variability, 0105 earth and related environmental sciences, General Environmental Science
Abstract

Quantifying human impacts on the N cycle and investigating natural ecosystem N cycling depend on the magnitude of inputs from natural biological nitrogen fixation (BNF). Here, we present two bottom-up approaches to quantify tree-based symbiotic BNF based on forest inventory data across the coterminous US plus SE Alaska. For all major N-fixing tree genera, we quantify BNF inputs using (1) ecosystem N accretion rates (kg N ha(−1) yr(−1)) scaled with spatial data on tree abundance and (2) percent of N derived from fixation (%N(dfa)) scaled with tree N demand (from tree growth rates and stoichiometry). We estimate that trees fix 0.30–0.88 Tg N yr(−1) across the study area (1.4–3.4 kg N ha(−1) yr(−1)). Tree-based N fixation displays distinct spatial variation that is dominated by two genera, Robinia (64% of tree-associated BNF) and Alnus (24%). The third most important genus, Prosopis, accounted for 5%. Compared to published estimates of other N fluxes, tree-associated BNF accounted for 0.59 Tg N yr(−1), similar to asymbiotic (0.37 Tg N yr(−1)) and understory symbiotic BNF (0.48 Tg N yr(−1)), while N deposition contributed 1.68 Tg N yr(−1) and rock weathering 0.37 Tg N yr(−1). Overall, our results reveal previously uncharacterized spatial patterns in tree BNF that can inform large-scale N assessments and serve as a model for improving tree-based BNF estimates worldwide. This updated, lower BNF estimate indicates a greater ratio of anthropogenic to natural N inputs, suggesting an even greater human impact on the N cycle.

Published
2020

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