Case Studies

Can water quality be forecast just like the weather? For Dr. Quinn Thomas, that is the ultimate goal. He and fellow researchers tested a model for forecasting lake temperatures at six NEON lake sites. Their results were recently published in Frontiers in Ecology and the Environment. Thomas is also the Principal Investigator for the EFI-RCN NEON Forecasting Challenge, which seeks to create a community of ecological forecasters using NEON data to test forecasting models.

A unique and endangered ecosystem is nestled in the midst of the city of Oak Ridge, Tennessee. The Oak Ridge Barrens (The Barrens) is home to some of the few remaining pockets of natural prairie left in the state. Tennessee Citizens for Wilderness Planning (TCWP) wanted to perform a scientific inventory of the species composition in the cedar barrens. Margaret Cumberland, ecologist at NEON's Appalachians and Cumberland Plateau Domain (D07), facilitated an AGU-TEX community project to make this happen.

A recent paper in the Journal of Ecology explores correlations between tree species composition, fungal communities, and the relative abundance of different types of carbon in soil at seven NEON eastern forested field sites. Understanding these complex relationships could help researchers build better models of the carbon cycle in forested terrestrial ecosystems.

Dr. Lynn 'Marty' Martin and Dr. John Orrock are investigating the complex interactions between ticks, mice, and their habitats through a NEON Assignable Assets project. Their work could help illuminate how habitat quality, climate, and the behavioral choices of wild mice influence the spread of a dangerous pathogen.

How did the recent hurricanes in Puerto Rico impact the average body size of freshwater fish found in Puerto Rican rivers? Saniei and Crispo used data on fish body size from the two aquatic sites in the Atlantic Neotropical Domain (D04): CUPE and GUIL. They compared the data from before and after the hurricanes for four freshwater species.

High-tech sensors and monitors are transforming agriculture, enabling farmers to optimize yields and improve efficiency. Arable, a provider of smart digital agriculture solutions, is using the NEON program infrastructure through a NEON Assignable Assets project to calibrate and validate their sensor technology in various climate zones and ecosystems across the U.S.

Blue oaks are a keystone species in the woodlands of central California; however, climate change and land management practices have threatened populations in many areas. NEON's Sokaina Alkhafaji partnered with the Sierra Foothill Conservancy and the University of California Cooperative Extension to develop blue oak monitoring protocols that could be used by landowners and conservation organizations across the state.

Exactly how does biodiversity impact the stability of communities over time and across scales? A recent paper uses NEON data to explore the relationship between plant biodiversity and temporal stability at both local and regional scales.

A new paper in Journal of Animal Ecology uses NEON lidar data plus observational data to explore relationships between animal species richness and various local and regional variables. The goal? To determine which variables have the best predictive power for species richness at different scales.

A recent study published in Global Ecology and Biogeography explores how lidar and hyperspectral remote sensing data could be fused to improve estimates of plant biodiversity in temperate forests.

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.