Article

Standardized NEON organismal data for biodiversity research

Abstract

Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.
ARTICLE
Special Feature: Harnessing the NEON Data Revolution
Standardized NEON organismal data for
biodiversity research
Daijiang Li
1,2
| Sydne Record
3,4
| Eric R. Sokol
5,6
| Matthew E. Bitters
7
|
Melissa Y. Chen
7
| Y. Anny Chung
8
| Matthew R. Helmus
9
|
Ruvi Jaimes
10
| Lara Jansen
11
| Marta A. Jarzyna
12,13
| Michael G. Just
14
|
Jalene M. LaMontagne
15
| Brett A. Melbourne
7
| Wynne Moss
7
|
Kari E. A. Norman
16
| Stephanie M. Parker
5
| Natalie Robinson
5
|
Bijan Seyednasrollah
17
| Colin Smith
18
| Sarah Spaulding
6
|
Thilina D. Surasinghe
19
| Sarah K. Thomsen
20
| Phoebe L. Zarnetske
21,22
1
Department of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, USA
2
Center for Computation and Technology, Louisiana State University, Baton Rouge, Louisiana, USA
3
Department of Biology, Bryn Mawr College, Bryn Mawr, Pennsylvania, USA
4
Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, Maine, USA
5
National Ecological Observatory Network (NEON), Battelle, Boulder, Colorado, USA
6
Institute of Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, Colorado, USA
7
Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA
8
Departments of Plant Biology and Plant Pathology, University of Georgia, Athens, Georgia, USA
9
Integrative Ecology Lab, Center for Biodiversity, Department of Biology, Temple University, Philadelphia, Pennsylvania, USA
10
St. Edwards University, Austin, Texas, USA
11
Department of Environmental Science and Management, Portland State University, Portland, Oregon, USA
12
Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, Ohio, USA
13
Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio, USA
14
Ecological Processes Branch, U.S. Army ERDC CERL, Champaign, Illinois, USA
15
Department of Biological Sciences, DePaul University, Chicago, Illinois, USA
16
Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, California, USA
17
School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, USA
18
Environmental Data Initiative, University of Wisconsin-Madison, Madison, Wisconsin, USA
19
Department of Biological Sciences, Bridgewater State University, Bridgewater, Massachusetts, USA
20
Department of Integrative Biology, Oregon State University, Corvallis, Oregon, USA
21
Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA
22
Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA
Daijiang Li, Sydne Record, and Eric Sokol contributed equally to the work reported here.
Received: 9 April 2021 Revised: 9 February 2022 Accepted: 3 March 2022
DOI: 10.1002/ecs2.4141
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2022 The Authors. Ecosphere published by Wiley Periodicals LLC on behalf of The Ecological Society of America.
Ecosphere. 2022;13:e4141. https://onlinelibrary.wiley.com/r/ecs2 1of21
https://doi.org/10.1002/ecs2.4141
Correspondence
Daijiang Li
Email: dli30@lsu.edu
Funding information
National Science Foundation, Grant/
Award Numbers: 1832282, 1906144,
1926341, 1926567, 1926568, 1926598
Handling Editor: Jennifer K. Balch
Abstract
Understanding patterns and drivers of species distribution and abundance,
and thus biodiversity, is a core goal of ecology. Despite advances in recent
decades, research into these patterns and processes is currently limited by a
lack of standardized, high-quality, empirical data that span large spatial
scales and long time periods. The NEON fills this gap by providing freely
available observational data that are generated during robust and consis-
tent organismal sampling of several sentinel taxonomic groups within
81 sites distributed across the United States and will be collected for at least
30 years. The breadth and scope of these data provide a unique resource for
advancing biodiversity research. To maximize the potential of this opportu-
nity, however, it is critical that NEON data be maximally accessible and
easily integrated into investigatorsworkflows and analyses. To facilitate its
use for biodiversity research and synthesis, we created a workflow to pro-
cess and format NEON organismal data into the ecocomDP (ecological
community data design pattern) format that were available through the
ecocomDP R package; we then provided the standardized data as an R data
package (neonDivData). We briefly summarize sampling designs and data
wrangling decisions for the major taxonomic groups included in this effort.
Our workflows are open-source so the biodiversity community may: add
additional taxonomic groups; modify the workflow to produce datasets
appropriate for their own analytical needs; and regularly update the data
packages as more observations become available. Finally, we provide two
simple examples of how the standardized data may be used for biodiversity
research. By providing a standardized data package, we hope to enhance
the utility of NEON organismal data in advancing biodiversity research
and encourage the use of the harmonized ecocomDP data design pattern
for community ecology data from other ecological observatory networks.
KEYWORDS
biodiversity, data package, data product, EDI, organismal data, R, Special Feature:
Harnessing the NEON Data Revolution
INTRODUCTION (OR WHY
STANDARDIZED NEON
ORGANISMAL DATA)
A central goal of ecology is to understand the patterns
and processes of biodiversity, and this is particularly
important in an era of rapid global environmental change
(Blowes et al., 2019; Midgley & Thuiller, 2005). Such
understanding is only possible through studies that
address questions such as: How is biodiversity distributed
across large spatial scales, ranging from ecoregions to
continents? What mechanisms drive spatial patterns of
biodiversity? Are spatial patterns of biodiversity similar
among different taxonomic groups, and if not, why do we
see variation? How does community composition vary
across spatial and environmental gradients? What are the
local- and landscape-scale drivers of community struc-
ture? How and why do biodiversity patterns change over
time? Answers to such questions will enable better man-
agement and conservation of biodiversity and ecosystem
services.
Biodiversity research has a long history (Worm &
Tittensor, 2018), beginning with major scientific expedi-
tions (e.g., Alexander von Humboldt, Charles Darwin)
aiming to document global species lists after the establish-
ment of LinnaeussSystema Naturae (Linnaeus, 1758).
Beginning in the 1950s (Curtis, 1959;Hutchinson,1959),
researchers moved beyond documentation to focus on
2of21 LI ET AL.
quantifying patterns of species diversity and describing
mechanisms underlying their heterogeneity. Since the
beginning of this line of research, major theoretical break-
throughs (Brown et al., 2004;Harte,2011;Hubbell,2001;
MacArthur & Wilson, 1967) have advanced our under-
standing of potential mechanisms causing and
maintaining biodiversity. Modern empirical studies, how-
ever, have been largely constrained to local or regional
scales and focused on one or a few taxonomic groups,
because of the considerable effort required to collect obser-
vational data. There are now unprecedented numbers of
observations from independent small and short-term eco-
logical studies. These data support research into generali-
ties through syntheses and meta-analyses (Blowes
et al., 2019;Lietal.,2020; Vellend et al., 2013), but this
work is challenged by the difficulty of integrating data
from different studies and with varying limitations. Such
limitations include the following: differing collection
methods (methodological uncertainties); varying levels of
statistical robustness; inconsistent handling of missing
data; spatial bias; publication bias; and design flaws
(Koricheva & Gurevitch, 2014;Martinetal.,2012;
Nakagawa & Santos, 2012; Welti et al., 2021). Additionally,
it has historically been challenging for researchers to
obtain and collate data from a diversity of sources for use
in syntheses and/or meta-analyses (Gurevitch &
Hedges, 1999).
Barriers to meta-analyses have been reduced in recent
years to bring biodiversity research into the big data era
(Farley et al., 2018; Hampton et al., 2013) by large efforts to
digitize museum and herbarium specimens (e.g., iDigBio),
successful community science programs (e.g., iNaturalist,
eBird), technological advances (e.g., remote sensing, auto-
mated acoustic recorders), and long-running coordinated
research networks. Yet, each of these remedies comes with
its own limitations. For instance, museum/herbarium spec-
imens and community science records are increasingly
available, but are still incidental and unstructured in terms
of the sampling design, and exhibit marked geographic and
taxonomic biases (Beck et al., 2014; Geldmann et al., 2016;
Martin et al., 2012). Remote sensing approaches may cover
large spatial scales, but may also be of low spatial resolu-
tion and unable to reliably penetrate vegetation canopy
(Palumbo et al., 2017; Pricope et al., 2019). The standard-
ized observational sampling of woody trees by the US For-
est Services Forest Inventory and Analysis and of birds by
the US Geological Surveys Breeding Bird Survey has
been ongoing across the United States since 2001 and
1966, respectively (Bechtold & Patterson, 2005;Sauer
et al., 2017), but covers few taxonomic groups. The Long
Term Ecological Research Network (LTER) and Critical
Zone Observatory (CZO) both are hypothesis-driven
researcheffortsbuiltondecadesofpreviouswork(Jones
et al., 2021). While both provide considerable observational
and experimental datasets for diverse ecosystems and taxa,
their sampling and dataset design are tailored to their spe-
cific research questions and a priori standardization is not
possible. Thus, despite recent advances, biodiversity
research is still impeded by a lack of standardized, high-
quality, and open-access data spanning large spatial scales
and long time periods.
The recently established NEON provides continental-
scale observational and instrumentation data for a wide
variety of taxonomic groups and measurement streams.
Data are collected using standardized methods, across
81 field sites in both terrestrial and freshwater ecosys-
tems, and will be freely available for at least 30 years.
These consistently collected, long-term, and spatially
robust measurements are directly comparable throughout
the observatory, and provide a unique opportunity for
enabling a better understanding of ecosystem change and
biodiversity patterns and processes across space and
through time (Keller et al., 2008).
NEON data are designed to be maximally useful to
ecologists by aligning with FAIR principles (findable,
accessible, interoperable, and reusable; Wilkinson
et al., 2016). Despite meeting these requirements, however,
there are still challenges to integrating NEON organismal
data (e.g., occurrence and abundance of species) for repro-
ducible biodiversity research. For example, field names
may vary across NEON data products, even for similar
measurements; some measurements include sampling unit
information, whereas units must be decided for others.
These issues and inconsistencies may be overcome
through data cleaning and formatting, but understanding
how best to perform this task requires a significant invest-
ment in the comprehensive NEON documentation for
each data product involved in an analysis. Thoroughly
reading large amounts of NEON documentation is time-
consuming, and the path to a standard data format, as is
critical for reproducibility, may vary greatly between
NEON organismal data products and userseven for sim-
ilar analyses. Ultimately, this may result in subtle differ-
ences from study to study that hinder meta-analyses using
NEON data. A simplified and standardized format for
NEON organismal data would facilitate wider usage of
these datasets for biodiversity research. Furthermore, if
these data were formatted to interface well with datasets
from other coordinated research networks, more compre-
hensive syntheses could be accomplished to advance mac-
rosystems biology (Record et al., 2020).
One attractive standardized formatting style for
NEON organismal data is that of ecocomDP (ecological
community data design pattern; OBrien et al., 2021).
EcocomDP is the brainchild of members of the LTER net-
work, the Environmental Data Initiative (EDI), and
ECOSPHERE 3of21
NEON staff, and provides a model by which data from a
variety of sources may be easily transformed into consis-
tently formatted, analysis-ready community-level organ-
ismal data packages. This is done using reproducible
code that maintains dataset levels: L0 is incoming data,
L1 represents an ecocomDP data format and includes
tables representing observations, sampling locations, and
taxonomic information (at a minimum), and L2 is an out-
put format. Thus far, >70 LTER organismal datasets have
been harmonized to the L1 ecocomDP format through
the R package ecocomDP and more datasets are in the
queue for processing into the ecocomDP format by EDI
(OBrien et al., 2021).
We standardized NEON organismal data into the
ecocomDP format, and all R codes to process NEON data
products can be obtained through the R package
ecocomDP. For the major taxonomic groups included in
this initial effort, NEON sampling designs and major data
wrangling decisions are summarized in the Materials and
Methods section. We archived the standardized data in
the EDI Data Repository (https://doi.org/10.6073/pasta/
c28dd4f6e7989003505ea02e9a92afbf). To facilitate the
usage of the standardized datasets, we also developed
an R data package, neonDivData (https://github.com/
daijiang/neonDivData). We refer to the input data
streams provided by NEON as data products, and the
cleaned and standardized collection of data files provided
here as objects within the R data package, neonDiv-
Data, across this paper. Standardized datasets will be
maintained and updated as new data become available
from the NEON portal. We hope this effort will substan-
tially reduce data processing times for NEON data users
and greatly facilitate the use of NEON organismal data to
advance our understanding of Earths biodiversity.
MATERIALS AND METHODS
(OR HOW TO STANDARDIZE NEON
ORGANISMAL DATA)
There are many details to consider when starting to use
NEON organismal data products. Below, we outline key
points relevant to community-level biodiversity analyses
with regard to the NEON sampling design and decisions
that were made as the data products presented in this
paper were converted into the ecocomDP data model.
While the methodological sections below are specific to
particular taxonomic groups, there are some general points
that apply to all NEON organismal data products. First,
species occurrence and abundance measures as reported
in NEON biodiversity data products are not standardized
to sampling effort. Because there are often multiple
approaches to cleaning (e.g., dealing with multiple levels
of taxonomic resolution, interpretations of absences) and
standardizing biodiversity survey data, NEON publishes
raw observations along with sampling effort data to pre-
serve as much information as possible so that data users
can clean and standardize data as they see fit. The
workflows described here for 12 taxonomic groups repre-
sented in 11 NEON data products produce standardized
counts based on sampling effort, such as the count of indi-
viduals per area sampled or count standardized to the
duration of trap deployment, as described in Table 1.The
data wrangling workflows described below can be used to
access, download, and clean data from the NEON Data
Portal using the R ecocomDP package. To view a catalog
of available NEON data products in the ecocomDP format,
use ecocomDP::search_data (NEON). To import data
from a given NEON data product into your R environ-
ment, use ecocomDP::read_data(), and set the id argument
to the selected NEON to ecocomDP mapping workflow
(the L0 to L1 ecocomDP workflow IDin Table 1). This
will return a list of ecocomDP formatted tables and accom-
panying metadata. To create a flat data table (similar to
the R objects in the data package neonDivData described
in Table 2), use the ecocomDP::flatten_data() function.
Second, because different taxonomic groups have dif-
ferent sampling designs (see below for details), there are
no general data processing protocol that can be applied
to all taxonomic groups. Nevertheless, we tried to be as
consistent as possible during the data cleaning and stan-
dardization processes. All final data products have the
minimal information of locations (e.g., location_id, sit-
e_id, plot_id), species names (e.g., taxon_id, taxon_name,
taxon_rank), and presence/absence or abundance infor-
mation (e.g., variable_name, value, unit).
Third, our processes assume that NEON ensured
correct identifications of species. However, since records
may be identified to any level of taxonomic resolution,
and IDs above the genus level may not be useful for
most biodiversity projects, we removed records with
such IDs for groups that are relatively easy to identify
(i.e., fish, plant, small mammals) or have very few taxon
IDs that are above genus level (i.e., mosquito). However,
for groups that are hard to identify (i.e., algae, beetle,
bird, macroinvertebrate, tick, and tick pathogen), we
decided to keep all records regardless of their taxon ID
level. Users thus need to carefully consider which level
of taxon IDs they need to address their research ques-
tions. Another note regarding species names is the term
sp.versus spp.across NEON organismal data collec-
tions; the term sp.refers to a single morphospecies,
whereas the term spp.refers to more than one
morphospecies. This is an important point to consider
for community ecology or biodiversity analyses because
itmayadduncertaintytoestimatesofbiodiversity
4of21 LI ET AL.
metrics such as species richness. It is also important to
point out that NEON fuzzed taxonomic IDs to one
higher taxonomic level to protect species of concern. For
example, if a threatened Black-capped vireo (Vireo
atricapilla) is recorded by a NEON technician, the taxo-
nomic identification is fuzzed to Vireo in the data. Rare,
threatened, and endangered species are those listed as
such by federal and/or state agencies.
TABLE 1 Mapping NEON data products to ecocomDP formatted data packages with abundance standardized to observation effort.
Taxon group
L0 dataset
(NEON data
product ID)
Version of NEON data
used in this study
L0 to L1
ecocomDP
workflow ID
Primary variable
reported in
ecocomDP
observation table Units
Algae DP1.20166.001 https://doi.org/10.48443/
g2k4-d258; and
provisional data
neon.ecocomdp.
20166.001.001
Cell density Cells per square
centimeter or
cells per milliliter
Beetles DP1.10022.001 https://doi.org/10.48443/
xgea-hw23; and
provisional data
neon.ecocomdp.
10022.001.001
Abundance Count per trap day
Birds
a
DP1.10003.001 https://doi.org/10.48443/
88sy-ah40; and
provisional data
neon.ecocomdp.
10003.001.001
Cluster size Count of individuals
Fish DP1.20107.001 https://doi.org/10.48443/
7p84-6j62; and
provisional data
neon.ecocomdp.
20107.001.001
Abundance Catch per unit effort
Herptiles DP1.10022.001 https://doi.org/10.48443/
xgea-hw23; and
provisional data
neon.ecocomdp.
10022.001.002
Abundance Count per trap day
Macroinvertebrates DP1.20120.001 https://doi.org/10.48443/
gn8x-k322; and
provisional data
neon.ecocomdp.
20120.001.001
Density Count per square
meter
Mosquitoes DP1.10043.001 https://doi.org/10.48443/
c7h7-q918; and
provisional data
neon.ecocomdp.
10043.001.001
Abundance Count per trap hour
Plants
b
DP1.10058.001 https://doi.org/10.48443/
abge-r811&#12
https://doi.org/10.
48443/pr5e-1q60; and
provisional data
neon.ecocomdp.
10058.001.001
Percent cover Percent of plot area
covered by taxon
Small mammals DP1.10072.001 https://doi.org/10.48443/
h3dk-3a71; and
provisional data
neon.ecocomdp.
10072.001.001
Count Unique individuals
per 100 trap
nights per plot
per month
Tick pathogens
c
DP1.10092.001 https://doi.org/10.48443/
nygx-dm71; and
provisional data
neon.ecocomdp.
10092.001.001
Positivity rate Positive tests per
pathogen per
sampling event
Ticks DP1.10093.001 https://doi.org/10.48443/
7jh5-8s51; and
provisional data
neon.ecocomdp.
10093.001.001
Abundance Count per square
meter
Zooplankton DP1.20219.001 https://doi.org/10.48443/
150d-yf27; and
provisional data
neon.ecocomdp.
20219.001.001
Density Count per liter
Note: IDs in the L0 to L1 ecocomDP workflow ID columns were used in the R package ecocomDP to standardize organismal data.
a
Bird counts are reported per taxon per clusterobserved in each point count in the NEON data product and have not been further standardized to sampling
effort because standard methods for modeling bird abundance are beyond the scope of this paper.
b
Plantspercent cover value NA represents presence/absence data only.
c
Incidence rate per number of tests conducted is reported for tick pathogens.
ECOSPHERE 5of21
Fourth, NEON publishes data for additional organis-
mal groups, which were not included in this study given
the complexity of the data. For example, aquatic plants
(DP1.20066.001 and DP1.20072.001); benthic microbe
abundance (DP1.20277.001), metagenome sequences
(DP1.20279.001), marker gene sequences (DP1.20280.001),
and community composition (DP1.20086.001); surface water
microbe abundance (DP1.20278.001), metagenome sequences
(DP1.20281.001), marker gene sequences (DP1.20282.001),
and community composition (DP1.20141.001); and soil
microbe biomass (DP1.10104.001), metagenome sequences
(DP1.10107.001), marker gene sequences (DP1.10108.001),
and community composition (DP1.10081.001) were not con-
sidered here, though future work may utilize neonDivData to
align these datasets. Users interested in further explorations
of these data products may find more information on the
NEON data portal (https://data.neonscience.org/). Addition-
ally, concurrent work on a suggested bioinformatics pipeline
and how to run sensitivity analyses on user-defined parame-
ters for NEON soil microbial data, including code and
vignettes, is described in Qin et al. (2021).
Finally, it should be noted that NEON data collec-
tion efforts will continue well after this paper is publi-
shed and new changes to data collection methods
and/or processing may vary over time. Such changes
(e.g., change in the number of traps used for ground
beetle collection) or interruptions (e.g., due to COVID-
19) to data collection are documented in the issue log
for each data product on the NEON Data Portal and
the Readme text file that is included with NEON data
downloads. We will try our best to maintain and
update our standardized data products as long as
possible.
Terrestrial organisms
Breeding landbirds
NEON sampling design
NEON designates breeding landbirds as smaller birds
(usually exclusive of raptors and upland game birds) not
usually associated with aquatic habitats(Ralph, 1993;
Thibault, 2018). Most species observed are diurnal and
include both resident and migrant species. Landbirds are
surveyed via point counts in each of the 47 terrestrial
sites (Thibault, 2018). At most NEON sites, breeding
landbird points are located in 510, 3 3 grids (Figure 1),
which are themselves located in representative (domi-
nant) vegetation. Whenever possible, grid centers are co-
located with distributed base plot centers. When sites are
too small to support a minimum of five grids, separated
by at least 250 m from edge to edge, point counts are
completed at single points instead of grids. In these cases,
points are located at the southwest corners of distributed
base plots within the site. Five to 25 points may be sur-
veyed depending on the size and spatial layout of the site,
with exact point locations dictated by a stratified-random
spatial design that maintains a 250-m minimum separa-
tion between points.
Surveys occur during one or two sampling bouts per
season, at large and small sites, respectively. Observers go
to the specified points early in the morning and track
birds observed during each minute of a 6-min period, fol-
lowing a 2-min acclimation period, at each point
(Thibault, 2018). Each point count contains species, sex,
and distance to each bird (measured with a laser ran-
gefinder except in the case of flyovers) seen or heard.
TABLE 2 Summary of data products included in this study (as of 13 April 2022). Users can call the R objects in the R object column
from the R data package neonDivData to get the standardized data for specific taxonomic groups.
Taxon group R object No. species No. sites Start date End date
Algae data_algae 2279 34 2 July 2014 28 July 2020
Beetles data_beetle 768 47 3 July 2013 5 October 2021
Birds data_bird 577 47 5 June 2013 16 July 2021
Fish data_fish 158 28 29 March 2016 14 December 2021
Herptiles data_herp_bycatch 136 41 2 April 2014 6 October 2021
Macroinvertebrates data_macroinvertebrate 1373 34 1 July 2014 28 July 2021
Mosquitoes data_mosquito 131 47 9 April 2014 11 June 2021
Plants data_plant 6544 47 24 June 2013 13 October 2021
Small mammals data_small_mammal 149 46 19 June 2013 18 November 2021
Tick pathogens data_tick_pathogen 12 16 17 April 2014 1 October 2020
Ticks data_tick 19 46 2 April 2014 18 November 2021
Zooplankton data_zooplankton 166 7 2 July 2014 21 July 2021
6of21 LI ET AL.
Legend
Sensor staon
Water Chemistry Sampling
Groundwater Well
Meteorological Staon
Riparian Assessment
Reaeraon Drip
Reaeraon Sampling
Note: Fish, sediments, macroinvertebrates, plants,
and macroalgae are sampled based on site-specific
habitats and are not included in the figures.
(b) Wadeable Stream
(c) Nonwadeable River (d) Lake
(a) Terrestrial Observaon System
Biology and Morphology (1 km)
Sediment (500 m)
Reaeration (>500 m)
Biology and Morphology (1 km)
Sediment (500 m)
FIGURE 1 Generalized sampling schematics for Terrestrial Observation System (TOS) (a) and Aquatic Observation System (BD) plots.
For TOS plots, distributed, tower, and gradient plots, and locations of various sampling regimes are presented via symbols. For Aquatic
Observation System plots, wadeable streams, nonwadeable streams, and lake plots are shown in detail, with locations of sensors and
different sampling regimes presented using symbols. Panel (a) was originally published in Thorpe et al. (2016).
ECOSPHERE 7of21
Information relevant for subsequent modeling of detect-
ability is also collected during the point counts
(e.g., weather, detection method). The point count sur-
veys for NEON were modified from the Integrated Moni-
toring in Bird Conservation Regions field protocol for
spatially balanced sampling of landbird populations
(Pavlacky Jr et al., 2017).
Data wrangling decisions
The bird point count NEON data product
(DP1.10003.001) consists of a list of two associated data
frames: brd_countdata and brd_perpoint. The former
data frame contains information such as locations, spe-
cies identities, and their counts. The latter data frame
contains additional location information such as latitude
and longitude coordinates and environmental conditions
during the time of the observations. The separate data
frames are linked by eventID,which refers to the loca-
tion, date, and time of the observation. To prepare the
bird point count data for the L1 ecocomDP model, we
first merged both data frames into one and then removed
columns that are likely not needed for most community-
level biodiversity analyses (e.g., observer names). The
field taxon_id in the R object data_bird with the
neonDivData data package consists of the standard AOU
four-letter species code, although taxon_rank refers to
seven potential levels of identification (class, family,
genus, species, speciesGroup, subfamily, and subspecies).
Users can decide which level is appropriate; for example,
one might choose to exclude all unidentified birds
(taxon_id =UNBI), where no further details are available
below the class level (Aves sp.). The NEON sampling pro-
tocol has evolved over time, so users are advised to check
whether the samplingProtocolVersionassociated with
bird point count data (DP1.10003.001) fits their data
requirements and subset as necessary. Older versions of
protocols can be found at the NEON document library.
Ground beetle and herp bycatch
NEON sampling design
Ground beetle sampling is conducted via pitfall trapping,
across 10 distributed plots at each NEON site. The original
sampling design included the placement of a pitfall trap at
each of the cardinal directions along the distributed plot
boundary, for a total of four traps per plot and 40 traps per
site. In 2018, sampling was reduced via the elimination of
the North pitfall trap in each plot, resulting in 30 traps per
site (LeVan, Robinson, et al., 2019).
Beetle pitfall trapping begins when the temperature
has been >4C for 10 days in the spring and ends when
temperatures dip below this threshold in the fall.
Sampling occurs biweekly throughout the sampling sea-
son with no single trap being sampled more frequently
than every 12 days (LeVan, 2020a). After collection, the
samples are separated into carabid species and bycatch.
Invertebrate bycatch is pooled to the plot level and
archived. Vertebrate bycatch is sorted and identified by
NEON technicians, then archived at the trap level. Cara-
bid samples are sorted and identified by NEON techni-
cians, after which a subset of carabid individuals are sent
to be pinned and reidentified by an expert taxonomist.
More details can be found in Hoekman et al. (2017) and
LeVan, Robinson, et al. (2019).
Pitfall traps and sampling methods are designed by
NEON to reduce vertebrate bycatch (LeVan, Robinson,
et al., 2019). The pitfall cup is medium in size with a low
clearance cover installed over the trap entrance to mini-
mize large vertebrate bycatch. When a live vertebrate with
the ability to move on its own volition is found in a trap,
the animal is released. Live but moribund vertebrates are
euthanized and collected along with deceased vertebrates.
When 15 individuals of a vertebrate species are collected,
cumulatively, within a single plot, NEON may initiate
localized mitigation measures such as temporarily
deactivating traps and removing all traps from the site for
the remainder of the season. Thus, while herpetofaunal
(herp) bycatch is present in many pitfall samples it is
unclear how well these pitfall traps capture herp commu-
nity structure and diversitydue to these active efforts to
reduce vertebrate bycatch. Users of NEON herp bycatch
data should be aware of these limitations.
Data wrangling decisions
The beetle and herp bycatch data product identifier is
DDP1.10022.001.Carabid samples are recorded and
identified in a multistep workflow wherein a subset of
samples are passed on in each successive step. Individ-
uals are first identified by the sorting technician after
which a subset is sent on to be pinned. Some especially
difficult individuals are not identified by technicians dur-
ing sorting, instead of being labeled other carabid.The
identifications for those individuals are recorded with the
pinning data. Any individuals for which identification is
still uncertain are then verified by an expert taxonomist.
There are a few cases where an especially difficult identi-
fication was sent to multiple expert taxonomists, and they
did not agree on a final taxon; these individuals were
excluded from the dataset at the recommendation of
NEON staff.
Preference is given to expert identification whenever
available. However, these differences in taxonomic expertise
do not seem to cause systematic biases in estimating species
richness across sites, but nonexpert taxonomists are more
likely to misidentify non-native carabid species (Egli
8of21 LI ET AL.
et al., 2020). Beetle abundance is recorded for the sorted
samples by NEON technicians. To account for individual
samples that were later reidentified, the final abundance for
a species is the original sorting sample abundance minus
the number of individuals that were given a new ID.
Prior to 2018, trappingDays values were not included
for many sites. Missing entries were calculated as the
range from setDate through collectDate for each trap. We
also accounted for a few plots for which setDate was not
updated based on a previous collection event in the
trappingDays calculations. To facilitate easy manipula-
tion of data within and across bouts, a new boutID field
was created to identify all trap collection events at a site
in a bout. The original EventID field is intended to iden-
tify a bout, but has a number of issues that necessitates
the creation of a new ID. First, EventID does not corre-
spond to a single collection date but rather all collections
in a week. This is appropriate for the small number of
instances when collections for a bout happen over multi-
ple consecutive days (5% of bouts), but prevents analy-
sis of bout patterns at the temporal scale of a weekday.
The data here were updated so all entries for a bout cor-
respond to the date (i.e., collectDate) on which the major-
ity of traps are collected to maintain the weekday-level
resolution with as high of fidelity as possible, while all-
owing for easy aggregation within bouts and collectDates.
Second, there were a few instances in which plots within
a site were set and collected on the same day, but have
different EventIDs. These instances were all considered a
single bout by our new boutID, which is a unique combi-
nation of setDate, collectDate, and siteID.
Herpetofaunal bycatch (amphibian and reptile) in pit-
fall traps were identified to species or the lowest taxo-
nomic level possible within 24 h of recovery from the
field. To process the herp bycatch NEON data, we
cleaned trappingDays and the other variables and added
boutID as described above for beetles. The variable sam-
pleType in the bet_sorting table provides the type of ani-
mal caught in a pitfall trap as one of five types: carabid,
vert bycatch herp,”“other carabid,”“invert bycatch,
and vert bycatch mam.We filtered the beetle data
described above to only include the carabidand other
carabidtypes. For herps, we only kept the sampleType
of vert bycatch herp.Abundance data of beetle and
herp bycatch were standardized to be the number of indi-
viduals captured per trap day.
Mosquitoes
NEON sampling design
Mosquito specimens are collected at 47 terrestrial sites
across all NEON domains, and the data are reported in
NEON data product DP1.10043.001. Traps are distributed
throughout each site according to a stratified-random
spatial design used for all Terrestrial Observation System
sampling, maintaining stratification across dominant
(>5% of total cover) vegetation types (LeVan, 2020b). The
number of mosquito traps placed in each vegetation type
is proportional to its percent cover, until 10 total mos-
quito traps have been placed in the site. Mosquito traps
are typically located within 30 m of a road to facilitate
expedient sampling and are placed at least 300 m apart to
maintain independence.
Mosquito monitoring is divided into off-season and
field season sampling (LeVan, Paull, et al., 2019). Off-
season sampling begins after three consecutive zero-catch
field sampling bouts have occurred, and represents a
reduced sampling regime that is designed for the rapid
detection of when the next field season should begin and
to provide mosquito phenology data. Off-season sampling
is conducted at three dedicated mosquito traps spread
throughout each core site, while temperatures are >10C.
Once per week, technicians deploy traps at dusk and then
collect them at dawn the following day.
Field season sampling begins when the first mosquito
is detected during off-season sampling (LeVan, Paull,
et al., 2019). Technicians deploy traps at all 10 dedicated
mosquito trap locations per site. Traps remain out for a
24-h period or sampling bout, and bouts occur every 24
weeks at core and relocatable terrestrial sites, respec-
tively. During the sampling bout, traps are serviced twice
and yield one night-active sample, collected at dawn or
about 8 h after the trap was set, and 1 day-active sample,
collected at dusk or 16 h after the trap was set. Thus, a
24-h sampling bout yields 20 samples from 10 traps.
NEON collects mosquito specimens using Center for
Disease Control (CDC) CO
2
light traps (LeVan, Paull,
et al., 2019). These traps have been used by other public
health and mosquito-control agencies for a half-century
so that NEON mosquito data align across NEON field
sites and with existing long-term datasets. A CDC CO
2
light trap consists of a cylindrical insulated cooler that
contains dry ice, a plastic rain cover attached to a battery-
powered light/fan assembly, and a mesh collection cup.
During deployment, the dry ice sublimates and releases
CO
2
. Mosquitoes attracted to the CO
2
bait are sucked into
the mesh collection cup by the battery-powered fan,
where they remain alive until trap collection.
Following field collection, NEONs field ecologists pro-
cess, package, and ship the samples to an external labora-
tory where mosquitoes are identified to species and sex
(when possible). A subset of identified mosquitoes are
tested for infection by pathogens to quantify the presence/
absence and prevalence of various arboviruses. Some mos-
quitoes are set aside for DNA barcode analysis and long-
ECOSPHERE 9of21
term archiving. Particularly rare or difficult-to-identify
mosquito specimens are prioritized for DNA barcoding.
More details can be found in LeVan, Paull, et al. (2019).
Data wrangling decisions
The mosquito data product (DP1.10043.001) consists of
four data frames: trapping data (mos_trapping), sorting
data (mos_sorting), archiving data (mos_archivepooling),
and expert taxonomist processed data (mos_expert-
TaxonomistIDProcessed). We first removed rows (records)
with missing information about location, collection date,
and sample or subsample ID for all data frames. We then
merged all four data frames into one, wherein we only
kept records for target taxa (i.e., targetTaxaPresent =Y)
with no known compromised sampling condition
(i.e., sampleCondition =No known compromise). We
further removed a small number of records with species
identified only to the family level; all remaining records
were identified at least to the genus level. We estimated
the total individual count per trap hour for each species
within a trap as (individualCount/subsampleWeight)
totalWeight/trapHours. We then removed columns that
were not likely to be used for calculating biodiversity
values.
Small mammals
NEON sampling design
NEON defines small mammals based on taxonomic,
behavioral, dietary, and size constraints, and includes
any rodent that (1) is nonvolant; (2) is nocturnally active;
(3) forages predominantly aboveground; and (4) has a
mass >5 g, but <500600 g (Thibault et al., 2019). In
North America, this includes cricetids, heteromyids,
small sciurids, and introduced murids, but excludes
shrews, large squirrels, rabbits, or weasels, although indi-
viduals of these species may be incidentally captured.
Small mammals are collected at NEON sites using
Sherman traps, identified to species in the field, marked
with a unique tag, and released (Thibault et al., 2019).
Multiple 90 90 m trapping grids are set up in each ter-
restrial field site within the dominant vegetation type.
Each 90 90 m trapping grid contains 100 traps placed
in a pattern with 10 rows and 10 columns set 10 m apart.
Three of these 90 90 m grids per site are designated
pathogen (as opposed to diversity) grids, and additional
blood sampling is conducted here.
Small mammal sampling occurs in bouts, with a bout
comprised of three consecutive (or nearly consecutive)
nights of trapping at each pathogen grid and one night of
trapping at each diversity grid. The timing of sampling
occurs within 10 days before or after the new moon. The
number of bouts per year is determined by site type: Core
sites are typically trapped for six bouts per year (except
for areas with shorter seasons due to cold weather), while
relocatable sites are trapped for four bouts per year. More
information can be found in Thibault et al. (2019).
Data wrangling decisions
In the small mammal NEON data product (DP1.10072.001),
records are stratified by NEON site, year, month, and day
and represent data from both the diversity and pathogen
sampling grids. Capture records were removed if they were
not identified to genus or species (e.g., if the species name
was denoted as either/oror as family name), or if their
trap status is not 5captureor 4more than 1 capture
in one trap.Abundance data for each plot and month com-
bination were standardized to be the number of individuals
capturedper100trapnights.
Terrestrial plants
NEON sampling design
NEON plant diversity sampling is completed once or twice
per year (one or two bouts) in multiscale, 400-m
2
(20
20 m) plots (Barnett, 2019). Each multiscale plot is sub-
divided into four 100-m
2
(10 10 m) subplots that each
encompasses one or two sets of 10-m
2
(3.16 3.16 m) sub-
plots within which a 1-m
2
(1 1 m) subplot is nested. The
percent cover of each plant species is estimated visually in
the 1-m
2
subplots, while only species presences are docu-
mented in the 10- and 100-m
2
subplots.
To estimate plant percent cover by species, techni-
cians record this value for all species in a 1-m
2
subplot
(Barnett, 2019). Next, the remaining 9-m
2
area of the
associated 10-m
2
subplot is searched for the presence of
species. The process is repeated if there is a second 1- and
10-m
2
nested pair in the specific 100-m
2
subplot. Next,
the remaining 80-m
2
area is searched for the presence of
species; data can be aggregated for a complete list of spe-
cies present at the 100-m
2
subplot scale. Data for all four
100-m
2
subplots represent indices of species at the
400-m
2
plot scale. In most cases, species encountered in a
nested, finer scale, subplot are not rerecorded in any
corresponding larger subplotin order to avoid duplica-
tion. Plant species are occasionally recorded more than
once, however, when data are aggregated across all
nested subplots within each 400-m
2
plot, and these
require removal from the dataset. More details about the
sampling design can be found in Barnett et al. (2019).
NEON manages plant taxonomic entries with a mas-
ter taxonomy list that is based on the community stan-
dard, where possible. Using this list, synonyms for a
given species are converted to the currently used name.
10 of 21 LI ET AL.
The master taxonomy for plants is the USDA PLANTS
Database (USDA, NRCS, 2022;https://plants.usda.gov),
and the portions of this database included in the NEON
plant master taxonomy list are those pertaining to native
and naturalized plants present within the NEON sam-
pling area. A sublist for each NEON domain includes
those species with ranges that overlap the domain and
nativity designationsintroduced or nativein that part
of the range. If a species is reported at a location outside
of its known range, and the record proves reliable, the
master taxonomy list is updated to reflect the distribution
change. For more details on plant taxonomic handling,
see Barnett et al. (2019). For more on the NEON plant
master taxonomy list, see NEON.DOC.014042 (https://
data.neonscience.org/api/v0/documents/NEON.DOC.
014042vK).
Data wrangling decisions
In the plant presence and percent cover NEON data prod-
uct (DP1.10058.001), sampling at the 1 1 m scale also
includes observations of abiotic and nontarget species gro-
und cover (i.e., soil, water, and downed wood), so we
removed records with divDataType as otherVariables.
We also removed records whose targetTaxaPresent is N
(i.e., a nontarget species). Additionally, for all spatial reso-
lutions (i.e., 1-, 10-, and 100-m
2
data), any record lacking
information critical for combining data within a plot and
for a given sampling bout (i.e., plotID, subplotID,
boutNumber, endDate, or taxonID) was dropped from the
dataset. Furthermore, records without a definitive genus-
or species-level taxonID (i.e., those representing uni-
dentified morphospecies) were not included. To combine
data from different spatial resolutions into one data frame,
we created a pivot column entitled sample_area_m2 (with
possible values of 1, 10, and 100). Because of the nested
sampling design of the plant data, to capture all records
within a subplot at the 100-m
2
scale, we incorporated all
data from both the 1- and 10-m
2
scales for that subplot.
Similarly, to obtain all records within a plot at the 400-m
2
scale, we included all data from that plot. Species abun-
dance information was only recorded as area coverage
within 1 by 1 m subplots; however, users may use the fre-
quency of a species across subplots within a plot or plots
within a site as a proxy of its abundance if needed.
Ticks and tick pathogens
NEON sampling design
Tick sampling occurs in six distributed plots at each site,
which are randomly chosen in proportion to NLCD land
cover class (LeVan, Thibault, et al., 2019). Ticks are sam-
pled by walking the perimeter of a 40 40 m plot using a
11 m drag cloth. Ideally, 160 m is sampled (the shortest
straight line distance between corners), but the cloth can
be dragged around obstacles if a straight line is not possi-
ble. The acceptable total sampling area is between 80 and
180 m per plot. The cloth can also be flagged over vegeta-
tion when the cloth cannot be dragged across it. Ticks are
collected from the cloth and techniciansclothing at
appropriate intervals, depending on vegetation density,
and at every corner of the plot. Specimens are immedi-
ately transferred to a vial containing 95% ethanol.
Onset and offset of tick sampling coincide with phe-
nological milestones at each site, beginning within 2
weeksoftheonsetofgreen-upandendingwithin2
weeks of vegetation senescence (LeVan, Thibault,
et al., 2019). Sampling bouts are only initiated if the
high temperature on the two consecutive days prior to
planned sampling was >0C. Early-season sampling is
conducted on a low-intensity schedule, with one sam-
pling bout every 6 weeks. When more than five ticks of
any life stage have been collected within the last calen-
dar year at a site, sampling switches to a high-intensity
schedule at the sitewith one bout every 3 weeks. A
site remains on the high-intensity schedule until fewer
than five ticks are collected within a calendar year;
then, sampling reverts back to the low-intensity
schedule.
Ticks are sent to an external facility for identification
to species, life stage, and sex (LeVan, Thibault,
et al., 2019). A subset of nymphal ticks are additionally
sent to a pathogen testing facility. Ixodes species are tested
for Anaplasma phagocytophilum,Babesia microti,Borrelia
burgdorferi sensu lato, Borrelia miyamotoi,Borrelia may-
onii,otherBorrelia species (Borrelia sp.), and a Ehrlichia
muris-like agent (Pritt et al., 2017). Non-Ixodes species are
tested for A. phagocytophilum,Borrelia lonestari (and other
undefined Borrelia species), Ehrlichia chaffeensis,Ehrlichia
ewingii,Francisella tularensis,andRickettsia rickettsii.
Additional information about tick pathogen testing can
be found in the Tick Pathogen Testing SOP (https://data.
neonscience.org/api/v0/documents/UMASS_LMZ_
tickPathogens_SOP_20160829) for the NEON Tick-borne
Pathogen Status data product.
Data wrangling decisions
The tick NEON data product (DP1.10093.001) consists of
two dataframes: tck_taxonomyProcessed,hereafter
referred to as taxonomy data; and tck_fielddata,here-
after referred to as field data.Users should be aware of
some issues related to taxonomic ID. Counts assigned to
higher taxonomic levels (e.g., at the order-level Ixodida;
IXOSP2) are not the sum of lower levels; rather, they rep-
resent the counts of individuals that could not reliably be
assigned to a lower taxonomic unit. Samples that were
ECOSPHERE 11 of 21
not identified in the laboratory were assigned to the
highest taxonomic level (order Ixodida; IXOSP2). How-
ever, users could make an informed decision to assign
these ticks to the most probable group if a subset of indi-
viduals from the same sample were assigned to a lower
taxonomy.
To clean the tick data, we first removed surveys and
samples not meeting quality standards. In the taxonomy
data, we removed samples where sample condition was
not listed as OK(<1% of records). In the field data, we
removed records where samples were not collected due
to logistical concerns (10%). We then combined male and
female counts in the taxonomy table into one adult
class. The taxonomy table was reformatted so that every
row contained a sampleID and counts for each species
life stages were separate columns (i.e., wide format).
Next, we joined the field data to the taxonomy data, using
the sample ID to link the two tables. When joining, we
retained field records where no ticks were found in the
field, and thus, there were no associated taxonomy data.
In drags where ticks were not found, counts were given
zeros. All counts were standardized by area sampled.
Prior to 2019, both field surveyors and laboratory tax-
onomists enumerated each tick life stage; consequently,
in the joined dataset there were two sets of counts (field
countsand laboratory counts). However, starting in
2019, counts were performed by taxonomists rather than
field surveyors. Field surveys conducted after 2019 no
longer have field counts. Users of tick abundance data
should be aware that this change in protocol has several
implications for data wrangling and for analysis. First,
after 2019, tick counts are no longer published at the
same time as field survey data. Subsequently, some field
records from the most recent years have tick presence
recorded (targetTaxaPresent =Y), but do not yet have
associated counts or taxonomic information and so the
counts are still listed as NA. Users should be aware that
counts of zero are therefore published earlier than posi-
tive counts. We strongly urge users to filter data to those
years where there are no counts pending.
Thesecondmajorissueisthatinyearswhereboth
field counts and laboratory counts were available, they
did not always agree (8% of records). In cases of dis-
agreement, we generally used laboratory counts in the
final abundance data, because this is the source of all
tick count data after 2019 and because life stage identi-
ficationwasmoreaccurate.However,therewereafew
exceptions where we used field count data. In some
cases, only a subsample of a certain life stage was coun-
ted in the laboratory, which resulted in higher field
counts than laboratory counts. In this case, we assigned
the additional unidentified individuals (e.g., the differ-
ence between the field and laboratory counts) to the
orderlevel(IXOSP2).IfqualitynotesfromNEON
described ticks being lost in transit, we also added the
additional lost individuals to the order level. There
were some cases (<1%) where the field counts were
greater than laboratory counts by more than 20% and
where the explanation was not obvious; we removed
theserecords.Wenotethatthemajorityofsamples
(85%) had no discrepancies between the laboratory or
field; therefore, this process could be ignored by users
whose analyses are not sensitive to exact counts.
The tick pathogen NEON data product (DP1.10092.001)
consists of two dataframes: tck_pathogen, hereafter referred
to as pathogen data; and tck_pathogenqa, hereafter
referred to as quality data.First, we removed any sam-
ples that had flagged quality checks from the quality
data and removed any samples that did not have a posi-
tive DNA quality check from the pathogen data.
Although the original online protocol aimed to test
130tickspersiteperyearfrommultipletickspecies,the
final sampling decision was to extensively sample
IXOSCA, AMBAME, and AMBSP species only because
IXOPAC and Dermacentor nymph frequencies were too
rare to generate meaningful pathogen data. Borrelia
burgdorferi and B. burgdorferi sensu lato tests were mer-
ged, since the former was an incomplete pathogen name
and refers to B. burgdorferi sensu lato as opposed to
sensu stricto (Rudenko et al., 2011). Tick pathogen data
are presented as positivity rate calculated as the number
of positive tests per number of tests conducted for a
given pathogen on ticks collected during a given sam-
pling event.
Aquatic organisms
Aquatic macroinvertebrates
NEON sampling design
Aquatic macroinvertebrate sampling occurs three times/
year at wadeable stream, river, and lake sites from spring
through fall. The timing of sampling is site-specific and
based on historical hydrological, meteorological, and
phenological data including dates of known ice cover,
growing degree days, and green-up and brown-down
(Cawley et al., 2016). Samplers vary by habitat and
include Surber, Hess, hand corer, modified kicknet, D-
frame sweep, and petite Ponar samplers (Parker, 2019).
Stream sampling occurs throughout the 1-km permitted
reach in wadeable areas of the two dominant habitat
types. Lake sampling occurs with a petite Ponar near
buoy, inlet and outlet sensors, and D-frame sweeps in
wadeable littoral zones. Riverine sample collections in
deep waters or near instrument buoys are made with a
12 of 21 LI ET AL.
petite Ponar, and in littoral areas are made with a D-
frame sweep or large woody debris sampler. In the field,
samples are preserved in pure ethanol, and later in the
domain support facility, glycerol is added to prevent the
samples from becoming brittle. Samples are shipped from
the domain facility to a taxonomy laboratory for sorting
and identification to the lowest possible taxon
(e.g., genus or species), and counts of each taxon per size
are made to the nearest millimeter.
Data wrangling decisions
Aquatic macroinvertebrate data contained in the NEON
data product DP1.20120.001 are subsampled and identified
to the lowest practical taxonomic level, typically genus,
by expert taxonomists in the inv_taxonomyProcessed
table, measured to the nearest millimeter size class, and
counted. Taxonomic naming has been standardized in the
inv_taxonomyProcessed file, according to NEONsmaster
taxonomy (https://data.neonscience.org/taxonomic-lists),
removing any synonyms. We calculated macroinvertebrate
density by dividing estimatedTotalCount (which includes
the corrections for subsampling in the taxonomy labora-
tory) by benthicArea from the inv_fieldData table to
return count per square meter of stream, lake, or river bot-
tom (Chesney et al., 2021).
Microalgae (periphyton and phytoplankton)
NEON sampling design
NEON collects periphyton samples from natural surface
substrata (i.e., cobble, silt, woody debris) over a 1-km
reach in streams and rivers, and in the littoral zone of
lakes. Various collection methods and sampler types are
used, depending on substrate (Parker, 2020). In lakes and
rivers, periphyton are also collected from the most domi-
nant substratum type in three areas within the littoral
(i.e., shoreline) zone. Prior to 2019, littoral zone periphy-
ton sampling occurred in five areas.
NEON collects three phytoplankton samples per sam-
pling date using Kemmerer or Van Dorn samplers. In riv-
ers, samples are collected near the sensor buoy and at
two other deep-water points in the main channel. For
lakes, phytoplankton are collected near the central sensor
buoy and at two littoral sensors. Where lakes and rivers
are stratified, each phytoplankton sample is a composite
from one surface sample, one sample from the meta-
limnion (i.e., middle layer), and one sample from the bot-
tom of the euphotic zone. For nonstratified lakes and
nonwadeable streams, each phytoplankton sample is a
composite from one surface sample, one sample just
above the bottom of the euphotic zone, and one mid-
euphotic zone sample, if the euphotic zone is >5 m deep.
All microalgal sampling occurs three times per year
(i.e., spring, summer, and fall bouts) in the same sampling
bouts as aquatic macroinvertebrates and zooplankton. In
wadeable streams, which have variable habitats (e.g., riffles,
runs, pools, and step pools), three periphyton samples are
collected per bout in the dominant habitat type (five sam-
ples collected prior to 2019) and three per bout in the sec-
ond most dominant habitat type. No two samples are
collected from the sample habitat unit (i.e., the same riffle).
Samples are processed at the domain support facility
and separated into subsamples for taxonomic analysis or
for biomass measurements. Aliquots shipped to an exter-
nal facility for taxonomic determination are preserved in
glutaraldehyde or Lugols iodine (before 2021). Aliquots
for biomass measurements are filtered onto glass-fiber fil-
ters and processed for ash-free dry mass (AFDM).
Data wrangling decisions
The periphyton, seston, and phytoplankton NEON data
product (DP1.20166.001) contains three dataframes for
algae containing information on algae taxonomic identifi-
cation, biomass, and related field data, which are here-
after referred to as alg_tax_long, alg_biomass, and
alg_field_data. Algae within samples are identified to the
lowest possible taxonomic resolution, usually species, by
contracting laboratory taxonomists. Some specimens can
only be identified to the genus or even class level,
depending on the condition of the specimen. Ten percent
of all samples are checked by a second taxonomist and are
noted in the qcTaxonomyStatus. Taxonomic naming has
been standardized in the alg_tax_long files, according to
NEONs master taxonomy, removing nomenclatural syno-
nyms. Abundance and cell/colony counts are determined
for each taxon of each sample with counts of cells or colo-
nies that are either corrected for sample volume or not
(as indicated by algalParameterUnit =cellsperBottle).
We corrected sample units of cellsperBottle to density
(Parker & Vance, 2020). First, we summed the preservative
volume and the laboratorys recorded sample volume for
each sample (from the alg_biomass file) and combined
that with the alg_tax_long file using sampleID as a com-
mon identifier. Where samples in the alg_tax_long file
were missing data in the perBottleSampleVolume field
(measured after receiving samples at the external labora-
tory), we estimated the sample volume using NEON
domain laboratory sample volumes (measured prior to
shipping samples to the external laboratory). With this
updated file, we combined it with alg_field_data to have
the related field conditions, including benthic area sam-
pled for each sample. parentSampleID was used for
alg_field_data to join to the alg_biomass filessampleIDas
alg_field_data only has parentSampleID. We then calcu-
lated cells per milliliter for the uncorrected taxon of each
ECOSPHERE 13 of 21
sample, dividing algalParameterValue by the updated sam-
ple volume. Benthic sample results are expressed in terms
of area (i.e., multiplied by the field sample volume and
divided by benthic area sampled), in square meters. The
final abundance units are either cells per milliliter (phyto-
plankton and seston samples) or cells per square meter for
benthic samples.
The sampleIDs are child records of each parentSampleID
that will be collected as long as sampling is not impeded
(i.e., ice-covered or dry). In the alg_biomass file, there
should be only a single entry for each parentSampleID,
sampleID, and analysisType. Most often, there were two
sampleIDs per parentSampleID with one for AFDM and tax-
onomy (analysis types). For the creation of the observation
table with standardized counts, we used only records from
the alg_biomass file with the analysisType of taxonomy. In
alg_tax_long, there are multiple entries for each sampleID
for each taxon by scientificName and algalParameter.
Fish
NEON sampling design
Fish sampling is carried out across 19 of the NEON
ecoclimatic domains, occurring in a total of 23 lotic
(stream) and 5 lentic (lake) sites. In lotic sites, up to
10 nonoverlapping reaches, each 70130 m long, are des-
ignated within a 1-km section of stream (Jensen
et al., 2019a). These include three constantly sampled
fixedreaches, which encompass all representative habi-
tats found within the 1-km stretch, and seven random
reaches that are sampled on a rotating schedule. In lentic
sites, 10 pie-shaped segments are established, with each
segment ranging from the riparian zone into the lake
center, therefore effectively capturing both nearshore and
offshore habitats (Jensen et al., 2019b). Three of the
10 segments are fixed and are surveyed twice a year, and
the remaining segments are random and are sampled
rotationally. The spatial layouts of these sites are
designed to capture spatial and temporal heterogeneity in
the aquatic habitats.
Lotic sampling occurs at three fixed and three ran-
dom reaches per sampling bout, and there are two bouts
per yearone in spring and one in fall. During each
bout, the fixed reaches are sampled via a three-pass elec-
trofishing depletion approach (Moulton II et al., 2002;
Peck et al., 2006), while the random reaches being sam-
pled are done so with a single-pass depletion approach.
Which random reaches are surveyed depends on the
year, with three of the random reaches sampled every
other year. All sampling occurs during daylight hours,
with each sampling bout completed within 5 days and
with a minimum 2-week gap in between two successive
sampling bouts. The initial sampling date is determined
using site-specific historical data on ice melting, water
temperature (or accumulated degree days), and riparian
peak greenness.
The lentic sampling design is similar to that discussed
above, with fixed segments being sampled twice per year
and random segments sampled twice per year on a rota-
tional basis (i.e., each random segment is not sampled
every year). Lentic sampling is conducted using three gear
types, with backpack electrofishing and mini-fyke nets
near the shoreline and gill nets in deeper waters. Backpack
electrofishing is done on a 4 25 m reach near the shore-
line via a three-pass (for fixed segments) or single-pass (for
random segments) electrofishing depletion approach
(Moulton II et al., 2002, Peck et al., 2006). All three passes
in a fixed sampling segment are completed on the same
night, with 30 min between successive passes. Electro-
fishing begins within 30 min of sunset and ceases within
30 min of sunrise, with a maximum of five passes per sam-
pling bout. A single gill net is also deployed within all seg-
ments being sampled, both fixed and random, for 12hin
either the morning or early afternoon. Finally, a fyke
(Baker et al., 1997) or mini-fyke net is deployed at each
fixed or random segments, respectively. Fyke nets are posi-
tioned before sunset and recovered after sunrise on the fol-
lowing day. Precise start and end times for electrofishing
and net deployments are documented by NEON techni-
cians at the time of sampling.
In all surveys, captured fish are identified to the
lowest practical taxonomic level, and morphometrics
(i.e., body mass and body length) are recorded for 50 indi-
viduals of each taxon before releasing. Relative abun-
dance for each fish taxon is also recorded by direct
enumeration (up to first 50 individuals) or estimation by
bulk counts (>50 individuals, i.e., by placing fish of a
given taxon into a dip net [i.e., net scoop], counting the
total number of specimens in the dip net, and then multi-
plying the total number of scoops of captured fish by the
counts from the first scoop).
Data wrangling decisions
Fish sampled via both electrofishing and trapping are identi-
fied at variable taxonomic resolutions (as fine as subspecies
level) in the field. Most identifications are made to the spe-
cies or genus level by a single field technician for a given
bout per site. Sampled fish are identified, measured, weighed,
and then released back to the site of capture. If field techni-
cians are unable to identify to the species level, such speci-
mens are identified to the finest possible taxonomic
resolution or assigned a morphospecies with a coarse-
resolution identification. The standard sources consulted for
identification and a qualifier for identification validity are
also documented in the fsh_perFish table. The column
14 of 21 LI ET AL.
bulkFishCount of the fsh_bulkCount table records relative
abundance for each species or the alternative next possible
taxon level (specified in the column scientificName).
Fish data (taxonomic identification and relative abun-
dance) are recorded per each sampling reach in streams
or per segment in lakes in each bout and documented in
the fsh_perFsh table (Monahan et al., 2020). The column
eventID uniquely identifies the sampling date of the year,
the specific site within the domain, a reach/segment
identifier, the pass number (i.e., number of electrofishing
passes or number of net deployment efforts), and the sur-
vey method. The eventID column helps tie all fish data
with stream reach/lake segment data or environmental
data (i.e., water quality data) and sampling effort data
(e.g., electrofishing and net set time). A reachID column
provided in the fsh_perPass table uniquely identifies sur-
veys done per stream reach or lake segment. The reachID
is nested within the eventID as well. We used eventID as
a nominal variable to uniquely identify different sam-
pling events and to join different, stacked fish data files
as described below.
The fish NEON data product (DP1.20107.001) consists of
fsh_perPass, fsh_fieldData, fsh_bulkCount, fsh_perFish, and
thecompletetaxontableforfish,forbothstreamandlake
sites. To join all reach-scale data, we first joined the
fsh_perPass with fsh_fieldData, and eliminated all bouts
where sampling was untenable. Subsequently, we joined the
reach-scale table with fsh_perFsh to add individual fish
counts and fish measurements. Then, to add bulk counts,
we joined the reach-scale table with fsh_bulkCount datasets,
andsubsequentlyaddedtaxonRank, which included the tax-
onomic resolution in the bulk-processed table. Afterward,
both individual-level and bulk-processed datasets were
appended into a single table. To include samples where no
fish were captured, we filtered the fsh_perPass table
retaining records where target taxa (fish) were absent, joined
it with fsh_fieldData, and finally merged it with the table
that contained both bulk-processed and individual-level
data. For each finer-resolution taxon in the individual-level
dataset, we considered the relative abundance as one since
each row represented a single individual fish. Whenever
possible, we substituted missingdatabycross-referencing
other data columns, omitted completely redundant data
columns, and retained records with genus- and species-level
taxonomic resolution. For the appended dataset, we also
calculated the relative abundance for each species per sam-
pling reach or segment at a given site. To calculate species-
specific catch per unit effort (CPUE), we normalized the
relative abundance by either average electrofishing time
(i.e., efTime, efTime2) or trap deployment time (i.e., the
difference between netEndTime and netSetTime). For trap
data, we assumed size of the traps used, water depths, num-
ber of netters used, and the reach lengths (a significant
proportion of bouts had reach lengths missing) to be compa-
rable across different sampling reaches and segments.
Zooplankton
NEON sampling design
Zooplankton samples are collected at seven NEON lake
sites across four domains. Zooplankton samples are col-
lected at the buoy sensor set (deepest location in the
lake) and at the two nearshore sensor sets using a verti-
cal tow net for locations deeper than 4 m and a Schin-
dler trap for locations shallower than 4 m (Parker &
Roehm, 2019). This results in three samples collected
per sampling day. Samples are preserved with ethanol
in the field and shipped from the domain facility to a
taxonomy laboratory for sorting and identification to
the lowest possible taxon (e.g., genus or species), and
counts of each taxon per size are made to the nearest
millimeter.
Data wrangling decisions
The NEON zooplankton data product (DP1.20219.001)
consists of dataframes for taxonomic identification and
related field data (Parker & Scott, 2020). Zooplankton in
NEON samples are identified at contracting laboratories to
the lowest possible taxonomic resolution, usually genus;
however, some specimens can only be identified to the
family (or even class) level, depending on the condition of
the specimen. Ten percent of all samples are checked by
two taxonomists and are noted in the qcTaxonomyStatus
column. The taxonomic naming has been standardized in
the zoo_taxonomyProcessed table, according to NEONs
master taxonomy, removing any synonyms. Density was
calculated using adjCountPerBottle and towsTrapsVolume
to correct count data to count per liter.
RESULTS (OR HOW TO GET AND
USE STANDARDIZED NEON
ORGANISMAL DATA)
All cleaned and standardized datasets can be obtained from
the R package neonDivData and from the EDI data repository
(https://doi.org/10.6073/pasta/c28dd4f6e7989003505ea02e
9a92afbf). Note that neonDivData included both stable
and provisional data released by NEON, while the data
repository in EDI only included stable datasets. If users
want to change some of the decisions to wrangle the data
differently, they can find the code in the R package
ecocomDP and modify them for their own purposes. If
this standardized version of NEON data was used, users
should cite this paper along with the citations provided by
ECOSPHERE 15 of 21
NEON for each taxonomic group. Such citations can be
found in the URLs presented in Table 1.
The data package neonDivData can be installed from
GitHub. Installation instructions can be found on the
GitHub webpage (https://github.com/daijiang/
neonDivData). Table 2shows a brief summary of all data
objects. To get data for a specific taxonomic group, we can
just call the objects in the R object column in Table 2.Such
data products include cleaned (and standardized if needed)
occurrence data for the taxonomic groups covered and are
equivalent to the observationtable of the ecocomDP data
format. If environmental information was provided by
NEON for some taxonomic groups, they are also included
in these data objects. Information such as latitude, longi-
tude, and elevation for all taxonomic groups was saved in
the neon_location object of the R package, which is equiva-
lent to the sampling_locationtable of the ecocomDP data
format. Information about species scientific names of all
taxonomic groups was saved in the neon_taxa object,
which is equivalent to the taxontable of the ecocomDP
data format.
To demonstrate the use of data packages, we used
data_plant to quickly visualize the distribution of species
richness of plants across all NEON sites (Figure 2). To
show how easy it is to get site-level species richness, we
presented the code used to generate the data for Figure 2
as supporting information.
Figure 2shows the utility of the data package for
exploring macroecological patterns. One of the most
well-known and studied macroecological patterns is the
latitudinal biodiversity gradient, wherein sites are more
species-rich at lower latitudes relative to higher latitudes;
temperature, biotic interactions, and historical biogeogra-
phy are potential reasons underlying these patterns
(Fischer, 1960; Hillebrand, 2004). Herbaceous plants of
NEON generally follow this pattern. The latitudinal pat-
tern for NEON small mammals is similar and is best
explained by increased niche space and declining similar-
ity in body size among species in lower latitudes, rather
than a direct effect of temperature (Read et al., 2018).
In addition to allowing for quick exploration of
macroecological patterns of richness at NEON sites, the
250
500
750
1000
Species
Richness
Species
Richness
250
500
750
1000
0
250
500
750
1000
20 40 60
Latitude
Species Richness
FIGURE 2 Plant species richness mapped across NEON terrestrial sites. The inset scatterplot shows latitude on the x-axis and species
richness on the y-axis, with red points representing sites in Puerto Rico and Hawaii.
16 of 21 LI ET AL.
data packages presented in this paper enable investiga-
tion of the effects of taxonomic resolution on diversity
indices since taxonomic information is preserved for
observations under family level for all groups. The degree
of taxonomic resolution varies for NEON taxa depending
on the diversity of the group and the level of taxonomic
expertise needed to identify an organism to the species
level, with more diverse groups presenting a greater chal-
lenge. Beetles are one of the most diverse groups of
organisms on Earth and wide-ranging geographically,
making them ideal bioindicators of environmental
change (Rainio & Niemelä, 2003). To illustrate how the
use of the beetle data package presented in this paper
enables NEON data users to easily explore the effects of
taxonomic resolution on community-level taxonomic
diversity metrics, we calculated Josts diversity indices
(Jost, 2006) for beetles at the Oak Ridge National Labora-
tory (ORNL) NEON site for data subset at the genus, spe-
cies, and subspecies level. To quantify biodiversity, we
used Josts indices, which are essentially Hill numbers
that vary in how abundance is weighted with a parameter
q. Higher values of qgive lower weights to low-
abundance species, with q=0 being equivalent to species
richness and q=1 representing the effective number of
species given by the Shannon entropy. These indices are
plotted as rarefaction curves, which assess the sampling
efficacy. When rarefaction curves asymptote, they suggest
that additional sampling will not capture additional taxa.
Statistical methods presented by Chao et al. (2014) pro-
vide estimates of sampling efficacy beyond the observed
data (i.e., extrapolated values shown by dashed lines in
Figure 3). For the ORNL beetle data, Josts indices calcu-
lated with higher values of q(i.e., q> 0) indicated sam-
pling has reached an asymptote in terms of capturing
diversity regardless of taxonomic resolution (i.e., genus,
species, and subspecies). However, rarefaction curves for
q=0, which is equivalent to species richness, do not
asymptote, even with extrapolation. These plots suggest
that if a researcher is interested in low-abundance, rare
species, then the NEON beetle data stream at ORNL may
need to mature with additional sample collections over
time before confident inferences may be made, especially
below the taxonomic resolution of the genus.
DISCUSSION (OR HOW TO
MAINTAIN AND UPDATE
STANDARDIZED NEON
ORGANISMAL DATA)
NEON organismal data hold enormous potential to
understand biodiversity change across space and time
(Balch et al., 2019; Jones et al., 2021). Multiple biodiver-
sity research and education programs have used NEON
data even before NEON became fully operational in May
2019 (e.g., Farrell & Carey, 2018; Read et al., 2018). With
the expected long-term investment to maintain NEON
over the next 30 years, NEON organismal data will be an
invaluable tool for understanding and tracking biodiver-
sity change. NEON data are unique relative to data col-
lected by other similar networks (e.g., LTER, CZO)
because observation collection protocols are standardized
genus species subspecies
0 1000 2000 3000 0 1000 2000 3000 0 1000 2000 3000
0
30
60
90
120
No. individuals
Species diversity
0
1
2
interpolated
extrapolated
FIGURE 3 Rarefaction of beetle abundance data from collections made at the Oak Ridge National Laboratory NEON site from 2014 to
2020 generated using the beetle data package presented in this paper and the iNEXT package in R (Hsieh et al., 2016) based on different
levels of taxonomic resolution (i.e., genus, species, and subspecies). Different colors indicate Josts indices with differing values of
q(Jost, 2006).
ECOSPHERE 17 of 21
across sites, enabling researchers to address macroscale
questions in environmental science without having to
synthesize disparate datasets that differ in collection
methods (Jones et al., 2021). The data package presented
in this paper holds great potential in making NEON data
easier to use and more comparable across studies.
Whereas the data collection protocols implemented by
NEON staff are standardized, the decisions NEON data
users make in wrangling their data after downloading
NEONs open data will not necessarily be similar unless
the user community adopts a community data standard,
such as the ecocomDP data model. Adopting such a data
model early on in the life of the observatory will ensure
that the results of studies using NEON data will be com-
parable and thus easier to synthesize. By providing a
standardized and easy-to-use data package of NEON
organismal data, our effort here will significantly lower
the barriers to use the NEON organismal data for biodi-
versity research by many current and future researchers
and will ensure that studies using NEON organismal data
are comparable.
All code for the Data Wrangling Decisions are avail-
able within the R package ecocomDP (https://github.
com/EDIorg/ecocomDP). Users can modify the code if
they need to make different decisions during the data
wrangling process and update our workflows in our code
by submitting a pull request to our GitHub repository. If
researchers wish to generate their own derived organis-
mal datasets from NEON data with slightly different deci-
sions than the ones outlined in this paper, we
recommend that they use the ecocomDP framework, con-
tribute their workflow to the ecocomDP R package,
upload the data to the EDI repository, and cite their data
with the discoverable DOI given to them by EDI. Note
that the ecocomDP data model was intended for commu-
nity ecology analyses and may not be well suited for
population-level analyses. In a similar vein, researchers
should ensure that they have considered sample size
issues before fitting any models with these data. See
Barnett (2019) for a review of the NEON organismal sam-
pling design that contains important insights related to
sample size issues.
Because ecocomDP is an R package to access and for-
mat datasets following the ecocomDP format, we devel-
oped an R data package neonDivData to host and
distribute the standardized NEON organismal data
derived from ecocomDP. A separate dedicated data pack-
age has several advantages. First, it is easier and ready to
use and saves time for users to run the code in ecocomDP
to download and standardize NEON data products. Sec-
ond, it is also easy to update the data package when new
raw data products are uploaded by NEON to their data
portal, and the updating process does not require any
change in the ecocomDP package. This is ideal because
ecocomDP provides harmonized data from other sources
besides NEON. Third, the GitHub repository page of
neonDivData can serve as a discussion forum for
researchers regarding the NEON data products without
competing for attention in the ecocomDP GitHub reposi-
tory page. By opening issues on the GitHub repository,
users can discuss and contribute to improve our
workflow of standardizing NEON data products. Users
can also discuss whether there are other data models that
the NEON user community should adopt at the inception
of the observatory. As the observatory moves forward,
this is an important discussion for the NEON user com-
munity and NEON technical working groups to promote
the synthesis of NEON data with data from other efforts
(e.g., LTER, CZO, AmeriFlux, the International LTER,
National Phenology Network, and Long Term Agricul-
tural Research Network). Note that the standardized
datasets that are stable (defined by NEON as stable
release) were archived at EDI and some of the above
advantages also apply to the data repository at EDI.
The derived data products presented here collectively
represent hundreds of hours of work by members of our
teama group that met at the NEON Science Summit in
2019 in Boulder, Colorado, and consists of researchers
and NEON science staff. Just as it is helpful when work-
ing with a dataset to either have collected the data or be
in close correspondence with the person who collected
the data, final processing decisions were greatly informed
by conversations with NEON science staff and the NEON
user community. Future opportunities that encourage
collaborations between NEON science staff and the
NEON user community will be essential to achieve the
full potential of the observatory data.
CONCLUSION
Macrosystems ecology (sensu Heffernan et al., 2014)isat
the start of an exciting new chapter with the decades-long
awaited buildout of NEON completed and standardized
data streams from all sites in the observatory becoming
publicly available online. As the research community
embarks on discovering new scientific insights from
NEON data, it is important that we make our analyses
and all derived data as reproducible as possible to ensure
that connections across studies are possible. Harmonized
datasets will help in this endeavor because they naturally
promote the collection of provenance as data are collated
into derived products (OBrien et al., 2021; Reichman
et al., 2011). Harmonized data also make synthesis easier
because efforts to clean and format data leading up to
analyses do not have to be repeatedly performed by
18 of 21 LI ET AL.
individual researchers (OBrien et al., 2021). The data
standardizing processes and derived data package pres-
ented here illustrate a potential path forward in achieving
a reproducible framework for data derived from NEON
organismal data for ecological analyses. This derived data
package also highlights the value of collaboration
between the NEON user community and NEON staff for
advancing NEON-enabled science. Finally, the extension
of the ecocomDP harmonized data design pattern to data
from other ecological research and observatory networks
(e.g., the Brazilian Network of Networks; de Oliveira
Roque et al., 2018) and South African Environment
Observation Network (Van Jaarsveld et al., 2007) has the
potential to enable community ecologists to better syn-
thesize data from across the globe.
ACKNOWLEDGMENTS
This work is a result of participating in the first NEON Sci-
ence Summit in 2019 and an internship program through
the St. Edwards Institute for Interdisciplinary Science
(i4) funded through a National Science Foundation (NSF)
award under grant number 1832282. The authors acknowl-
edge support from the NSF Award 1906144 to attend the
2019 NEON Science Summit. Additionally, the authors
acknowledge support from the NSF DEB 1926568 to Sydne
Record, NSF DEB 1926567 to Phoebe Zarnetske, NSF DEB
1926598 to Marta A. Jarzyna, and NSF DEB 1926341 to
Jalene M. LaMontagne. Comments from NEON staff
(Katie LeVan, Dylan Mpnahan, Sata Paull, Dave Barnett,
and Sam Simkin), Margaret OBrien, and Tad Dallas
greatly improved this work. The NEON is a program spon-
sored by the NSF and operated under a cooperative agree-
ment by Battelle Memorial Institute. This material is based
in part upon work supported by the NSF through the
NEON Program.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
No data were collected for this study. All original data
were collected by NEON and are publicly available at
NEONs data portal. We standardized such data and pro-
vided them as an R package available from Zenodo:
https://doi.org/10.5281/zenodo.6419751. Other data (Li
et al., 2022) are available from the Environmental Data
Initiative (EDI) Data Portal: https://doi.org/10.6073/
pasta/c28dd4f6e7989003505ea02e9a92afbf.
ORCID
Daijiang Li https://orcid.org/0000-0002-0925-3421
Sydne Record https://orcid.org/0000-0001-7293-2155
Eric R. Sokol https://orcid.org/0000-0001-5923-0917
Y. Anny Chung https://orcid.org/0000-0001-5207-2872
Matthew R. Helmus https://orcid.org/0000-0003-3977-
0507
Marta A. Jarzyna https://orcid.org/0000-0002-6734-
0566
Michael G. Just https://orcid.org/0000-0003-2493-9269
Jalene M. LaMontagne https://orcid.org/0000-0001-
7713-8591
Brett A. Melbourne https://orcid.org/0000-0002-8843-
4131
Wynne Moss https://orcid.org/0000-0002-2813-1710
Kari E. A. Norman https://orcid.org/0000-0002-2029-
2325
Bijan Seyednasrollah https://orcid.org/0000-0002-5195-
2074
Colin Smith https://orcid.org/0000-0003-2261-9931
Sarah Spaulding https://orcid.org/0000-0002-9787-
7743
Thilina D. Surasinghe https://orcid.org/0000-0001-
8758-072X
Sarah K. Thomsen https://orcid.org/0000-0001-5964-
7536
Phoebe L. Zarnetske https://orcid.org/0000-0001-6257-
6951
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