In August 2021, NEON partnered with DeLeaves again (first collaboration in 2019) for a campaign at the Smithsonian Environmental Research Center (SERC) field site to validate the scalability of a drone technique for sampling foliage from tall canopies.
NEON is committed to improving the quality of data we provide to the user community. After receiving feedback from users, and in consultation with the Aquatic Technical Working Group, we are moving forward with changes to the Water quality (DP1.20288.001) data product.
NEON recently reviewed and corrected land cover maps for five field sites using data collected by the Airborne Observation Platform and analyzed by NEON staff scientists. The new maps provide a more accurate view of land cover distribution at the sites and will be used to inform changes to our spatial sampling design.
The first set of tick-borne rodent pathogen data from the 5 pilot sites tested in 2020 are now available on the portal. Beginning in 2021, observatory-wide rodent pathogen testing will be for tick-borne pathogens, and data will be available through the rodent pathogen status, tick-borne data product. Prior to 2020, all of the rodent pathogen tests are for hantavirus and are available in the rodent pathogen status, hantavirus data product.
NEON biological sample data can now be found in the Global Biodiversity Information Facilities (GBIF) network. The partnership allows NEON data to be discovered and used alongside similar historical and global datasets, benefiting both current NEON data users and the international science community at large.
Community members in northern Wisconsin are learning how they can help monitor and control the spread of invasive plant species in their local area. A workshop organized by Ashley Spink, a senior field ecologist for the NEON program, was supported through NEON's partnership with the Thriving Earth Exchange program.
Cheat sheets provide a quick reference for functions, naming conventions, data formats, and other rules for using a code package. For new users of the package, we still recommend starting with the tutorials, but experienced users who need to check their function calls or look up details can get information quickly from the cheat sheet.
The plains of Texas and Oklahoma are home to rolling grasslands, expansive croplands, and more than 10 million head of cattle. In the Southern Plains (Domain 11), agriculture reigns supreme. Understanding how land management decisions and agricultural practices impact ecosystems is key to both maintaining agricultural productivity and protecting native habitats.
NEON's non-core field sites are not any more relocatable than the core sites. To better reflect the scientific intent of these sites, and with the concurrence of NSF, the NEON program is changing their terminology from "relocatable" to "gradient" sites.
An interim solution is starting to be implemented at all terrestrial sites that is expected to increase soil water content (DP1.00094.001) data availability, but may result in a step change in the soil moisture time series until all data are reprocessed through a new data processing system.
Planning to publish your work based on NEON data, samples, or resources through the Assignable Assets program? We just published a new webpage with helpful tips and guidelines!
NEON has joined forces with the Environmental Data Initiative to promote data accessibility and usability in the environmental sciences. The joint initiative will create tools, templates, and standards that will make it easier to synthesize data from NEON, the Long Term Ecological Research Network, and other networks and organizations.
If you can't choose between the mountains and the ocean, the Pacific Northwest (D16) may be the place for you. This coastal Domain features spectacular mountain ranges, vast conifer forests, and a rugged, rocky coastline. The NEON field sites, nestled into the Cascade Mountains of Washington and Oregon, offer an opportunity to see how different land management strategies impact this unique temperate ecosystem.
neonUtilities 2.1.1 is now available on CRAN! This new version fixes a small number of bugs, including the readr 2.0.0 incompatibility described in an earlier notification. Update is highly recommended.
Version 2.0.0 of the readr package was released July 20, 2021. This new version causes stackByTable() to error out in neonUtilities 2.1.0 and earlier. A new version of neonUtilities that resolves this issue will be released as soon as possible.
Cows and croplands dominate large portions of the U.S. How does this agriculture impact ecosystems? NEON field sites located on or near agricultural land – including some colocated with Long-Term Agroecosystem Research (LTAR) sites and Agricultural Research Stations – can help answer important questions about the interactions between agricultural activities and native ecosystems.
NEON staff participated in a five-session virtual workshop to generate creative strategies and new research collaborations centered on the fire science community. The workshop was designed to make steps towards improving understanding of different types of fires across temporal and spatial scales, predicting feedbacks between wildfire and living systems, and improving the representation of biological processes in models. Kaelin Cawley, a NEON research scientist and aquatic biogeochemist, brought the NEON perspective to the event.
If you like it hot, the Desert Southwest may be for you. From the iconic saguaro cactus stands of the Sonoran to the mesquite scrublands of the Chihuahuan, this region offers a diverse range of desert and mountainous landscapes to explore. Data from the NEON field sites will help scientists better understand the impact of human disturbances and climate change on these unique and fragile arid ecosystems.
Following the discovery that wind monitor orientation discrepancies were adversely affecting wind direction data in Wind speed and direction on lakes on-buoy, NEON has field-verified current wind monitor orientations at the buoy sites and updated the algorithm to account for the wind monitor orientation and approximate uncertainty atop the buoy mast.
While some people find bugs to be scary or gross, Dr. Roisin Stanbrook hopes to change that perception by showing how insects are critical to the conservation of our natural world. In addition to detailing her fascinating work with dung beetles, Stanbrook discussed how her non-traditional path to conservation ecology is an asset in the long run, and how she hopes to teach her students the same.
neonUtilities 2.1.0 is now available on CRAN! This new version adds new options for handling Release data, improves error messaging, and fixes bugs in flux footprints and microbe community composition. Update is highly recommended.
This is an update to the previous post concerning external lab surface water and groundwater nutrient data. Currently, sub-samples for nutrient analysis are being frozen and stored for future analysis with lower MDLs. The newly contracted external laboratory will begin receiving samples June 14, 2021 and all stored samples will be shipped by December 31, 2021. Data will be published approximately 90 days following sample receipt.
Macroinvertebrate metabarcoding (DP1.20126.001) and Zooplankton metabarcoding (DP1.20221.001) bioinformatics data in the tables inv_metabarcodeTaxonomy and zoo_metabarcodeTaxonomy tables are missing or have incorrectly labeled records for COI-F230 for samples collected in 2018 and 2019.
Surface water microbe cell count (DP1.20138.001) laboratory data for all aquatic sites from 9/2017 to 9/2020 were found to have incorrect calculations populating rawMicrobialAbundance data field. Data were recalculated, edited, and published to the data portal on April 5, 2021.
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.
