Case studies exemplify the impact that NEON can make on ecological research. Explore these stories that describe how our user community have made new, exciting discoveries about how our natural systems function.
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Dozens of individuals and teams have participated in the Ecological Forecasting Initiative Research Coordination Network (EFI-RCN)’s NEON Ecological Forecasting Challenge, which challenges people to create ecological forecasts using data from the NEON program. Educators across the country are using the Challenge with their undergraduate and graduate students.
Teams and individuals are creating ecological forecasts around five themes, including terrestrial water and carbon fluxes, beetle communities, tick populations, aquatic ecosystems, and plant phenology using NEON data.
As the Arctic climate warms, many areas where soils were previously frozen year-round are now experiencing cycles of freezing and thawing. Researchers set out to discover how these cycles are changing the physical structure of Arctic soils—using soil cores from NEON's Toolik Field Station (TOOL). The study is published in Geoderma.
Understanding how biological invasions start, the factors that allow invasive species to thrive, and their impact on native ecosystems are critical questions for ecology. A paper published in Ecosphere highlights the ways in which data from the NEON program could help ecologists explore the impacts and mechanisms of invasion.
Interest in Nature-based Climate Solutions is growing, in large part due to carbon markets that provide incentives to landowners. A new paper explores the use of eddy covariance flux towers and other measurement methods to get a better understanding of the impact of leveraging nature for addressing climate change.
At Pu'u Maka'ala Natural Area Reserve (PUUM) in Hawai`i, researchers have verified the discovery of two previously undescribed species of carabids (ground beetles). The two new species are both members of Mecyclothorax, a genus of ground beetles most diverse on volcanoes in the Hawaiian Islands and the Society Islands of French Polynesia.
Modern instrumentation and machine learning methods are increasingly used in the ecology community to supplement human effort. Could some of these methods be applied at the NEON field sites? A recent paper in Ecosphere explores the possibilities.
Using phenocams, Dr. Alesia Hallmark saw rhythmic and predictable branch movement in creosote bushes in New Mexico—even in dead branches. Now, she's looking through NEON phenocam data to see if she can document the phenomenon in other sites and species. Her results could upend common assumptions about movement—or lack thereof—in woody species.
Dr. Laura Meredith is working at NEON sites in Alaska to validate the use of carbonyl sulfide as a tracer molecule to better estimate of the amount of carbon taken up by plants. Her study was made possible through the NEON Assignable Assets Program and an NSF award.
A new study by Hakkenberg and Goetz uses NEON lidar and field observations to explore how climate mediates biodiversity-structure relationships (BSRs) across the U.S. Their findings could help improve biodiversity maps created with remote sensing data and better predict the impact of habitat degradation and climate change on biodiversity across disparate regions.
Can machine learning be used for accurate species identification of beetles and other invertebrates? Dr. Katie Marshall and Jarrett Blair at the University of British Columbia (UBC) and collaborators sought to answer this question using carabid beetle data from the NEON program. Eventually, they hope to leverage machine learning to identify other species caught in the NEON beetle pitfall traps. Machine learning could one day be used to classify unidentified species in the NEON bycatch (species caught other than the target species) and answer new questions about invertebrate diversity and abundance across North America.
How do you collect phenology data at a large scale for an elusive species like the deer mouse? Drs. Bryan McLean and Robert Guralnick combined mammal trapping data from the NEON program with a century of museum data to find insights into the environmental drivers of reproduction for small mammals.
Dr. Jennifer Balch, a Fire Ecologist at University of Colorado Boulder, is studying wildfire-impacted areas in the western U.S. to answer burning questions about forest recovery and carbon storage potential. Her work could lead to improved models of the impact of wildfires on atmospheric carbon levels and climate change.
An investigation into freshwater diatoms from the NEON aquatic field sites in Puerto Rico led to a reclassification of diatom taxa in the region, and the possible discovery of a new diatom species. A paper recently published in Phytotaxa details the results of the research, which was enabled by samples from the NEON Biorepository.
NASA monitors soil moisture levels and freeze/thaw conditions across the globe using a satellite orbiting 426 miles (685 km) above the Earth. To help validate and calibrate these satellite data, NASA relies on direct measurements taken by partners on the ground. Through a new collaboration with Battelle, soil moisture data collected at the National Ecological Observatory Network (NEON) field sites will now be part of those validation efforts.
How much of the water that enters terrestrial systems is used by plants for growth, and how much simply escapes back into the atmosphere unused? Chris Adkison, a researcher at Texas A&M University, used data from the NEON program to compare the accuracy of different methods of partitioning evaporation and transpiration in a Texas oak woodland.
A new study published in Nature Geoscience uses soil from NEON field sites across the continent to look for insights into how climate and ecosystem variables impact the formation and composition of SOM.
Arbuscular mycorrhizal (AM) fungi are found in nearly every ecosystem, quietly helping plants absorb nutrients from the soil. Dr. Bala Chaudhary wants to build a better model of how these vital ecosystem players disperse across the continent. She is using NEON’s Assignable Assets program to examine the role of aerial dispersal in AM fungal movement.
Dr. Zachary Kayler, an assistant professor in the Department of Soil and Water Systems at the University of Idaho, used NEON soil samples to test the ability of a widely-used soil health metric to detect changes from an extreme weather event - Hurricane Maria - in Puerto Rico.
Jeffery Cannon, a Forest Management Scientist at The Jones Center, is using remote sensing data from the NEON program to understand how longleaf pine forests are impacted by and recover from major weather events. He and his colleagues will use the results to develop tools to help forest managers plan restoration and conservation efforts.
Andrew Fricker, used remote sensing data from the NEON Airborne Observation Platform to train a neural net to classify tree species in a Sierra Nevada forest. He and his coauthors describe their approach in Remote Sensing: “A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery.”
Chris Gough, an associate professor of biology at Virginia Commonwealth University (VCU), is using data from the NEON program to explore relationships between forest structure, biodiversity, and other characteristics and their ability to sequester carbon. His collaborative work with PIs from the University of Connecticut and Purdue University was recently published in Ecology: “High Rates of Primary Production in Structurally Complex Forests."
Understanding why tick populations are increasing, and why some species are spreading into new geographic areas, is of critical importance to public health. In a recent study, researchers used NEON data to develop a model of tick population dynamics at the Ordway Swisher Biological Station field site.
A team led by NEON scientists David Hulslander and Jessica Bolis has developed a method to map tree mortality with an unprecedented level of detail using hyperspectral remote sensing data from the NEON Airborne Observational Platform and a novel imaging algorithm.
A new modeling approach could allow researchers to use remote sensing lidar data to predict small mammal biodiversity based on the structure of vegetation in an area. The study was led by Sarah Schooler, now a Ph.D. candidate at State University of New York (SUNY)–Syracuse, and Harold Zald of the Humboldt State University Department of Forestry and Wildland Resources. Lidar Prediction of Small Mammal Diversity in Wisconsin, published in Remote Sensing, explores how measurements of vegetation structure created with lidar data could be used to predict the diversity of small mammal communities.
Dr. Phoebe Zarnetske, an Assistant Professor in the Department of Integrative Biology at Michigan State University (MSU), is using data from the NEON sites to investigate patterns in biodiversity and species traits across the continent. Her goal is to better understand the drivers that influence species distributions and community assembly.
Kyla Dahlin and her team are using Airborne Remote Sensing data from five NEON sites to develop detailed 3D maps of forest structure. Their work, which was funded by the National Science Foundation (NSF), could provide new insights into the carbon storage potential of forests.
Land use changes and habitat loss have resulted in an overall loss of biodiversity across much of the country. Luis Carrasco, a post-doctoral fellow at NIMBioS, is leveraging NEON data to better understand the relationships between vegetation structure and density and bird biodiversity in forested ecosystems.
Adlafia neoniana (Naviculaceae) may be tiny, but it's got a big name to live up to. It's the first new species to be discovered on a NEON field site and named after the NEON program. So what is this newly discovered organism? A single-celled aquatic alga with a cell wall made of silica, known as a diatom.