Data collected from research networks present
opportunities to test theories and develop models about factors responsible
for the long-term persistence and vulnerability of soil organic matter
(SOM). Synthesizing datasets collected by different research networks
presents opportunities to expand the ecological gradients and scientific
breadth of information available for inquiry. Synthesizing these data is
challenging, especially considering the legacy of soil data that have
already been collected and an expansion of new network science initiatives.
To facilitate this effort, here we present the SOils DAta Harmonization
database (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets from
multiple research networks. SoDaH is built on several network science
efforts in the United States, but the tools built for SoDaH aim to provide
an open-access resource to facilitate synthesis of soil carbon data.
Moreover, SoDaH allows for individual locations to contribute results from
experimental manipulations, repeated measurements from long-term studies,
and local- to regional-scale gradients across ecosystems or landscapes.
Finally, we also provide data visualization and analysis tools that can be
used to query and analyze the aggregated database. The SoDaH v1.0 dataset is
archived and available
at 10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020).
Introduction
Soil organic matter (SOM) contains 2–3 times the amount of carbon
(C) as the atmosphere and terrestrial vegetation combined, yet adequately
describing SOM dynamics in numerical models remains a challenge (Jackson et
al., 2017). Recent biogeochemical research has attempted to understand how
climate, biota, soil chemistry, and mineralogy interact to determine SOM
stabilization and persistence (Schmidt et al., 2011; Lehmann and Kleber,
2015). Emerging theories also highlight how interactions among these factors
affect the production and apparent stabilization of microbial residues
(Grandy and Neff, 2008; Cotrufo et al., 2013; Kallenbach et al., 2016).
Notably, these new studies emphasize the importance of soil mineralogy and
physical structure in limiting microbial access to otherwise decomposable
substrates (Dungait et al., 2012; Miltner et al., 2012; Schimel and Schaeffer,
2012; Sulman et al., 2014).
Datasets that span environmental and edaphic gradients are critical for
constraining soil C estimates and developing and testing theoretical and
numerical models that are based on these ideas (Wieder and Allison et al., 2015; Luo et al., 2016; Harden et al., 2018; Sulman et al., 2018; Malhotra et
al., 2019). Data synthesized across scientific networks, notably those with
long-term observations and manipulations, are especially useful for
establishing general patterns across broad environmental gradients. These
insights and the primary data are valuable for model development. For
example, efforts to synthesize and archive results from the Long-Term
Intersite Decomposition Experiment Team (LIDET; Gholz et al., 2000; Parton
and Silver, 2007; Adair et al., 2008; Harmon, 2013) provide a valuable
benchmark for parameterizing and evaluating models with litter decomposition
data (Bonan et al., 2013; Wieder and Grandy et al., 2015; Kyker-Snowman et al., 2020). Elsewhere, Zhang et al. (2020) used data from three research networks
in Europe, China, and Australia to parameterize and evaluate two soil carbon
models. Providing similar data syntheses with information on soil carbon and
associated covariates (e.g., climate, productivity, and soil physical and
chemical properties) in public databases is critical to advancing
understanding soil biogeochemistry.
Coordinated research activities and the expansion of research network
infrastructure are broadening the scope and breadth of information measured
across sites in ways that can advance SOM science (Hinckley et al., 2016;
Baatz et al., 2018; Richter et al., 2018; Weintraub et al., 2019; Lajtha et al., 2018). With a 40-year investment in continuous or multi-year measurements
and a rich legacy of manipulative experiments, the Long-Term Ecological
Research (LTER) Network provides a publicly available data archive through
the Environmental Data Initiative (EDI;
https://portal.edirepository.org/nis/home.jsp, last access: 28 April 2021). The LTER network has an
advantage of hosting diverse research experiments, but because each site in
the network has different research foci data are not collected or reported
in a consistent manner (Billings et al., 2021, but see Zak et al., 1994; Frank
et al., 2012). By contrast, new investments in networks like the National
Ecological Observatory Network (NEON) provide a top-down, standardized
framework for data collection across sites. Synthesizing data from across
LTER, NEON, and other research networks presents unique opportunities to
deepen our general understanding of soil biogeochemistry.
Here, we present a flexible database designed to harmonize diverse SOM
datasets from across research networks. We aim to provide an open-access
resource to facilitate the synthesis of soil C data. This data resource can
expand to accommodate legacy datasets as they are identified and incorporate
new data products as they become available. This data infrastructure is
critical to advance understanding in SOM dynamics at a time when the
theoretical foundations and numerical representations of soil biogeochemical
processes are rapidly evolving.
The SoDaH database
Our team created the SOils DAta Harmonization (SoDaH) database to bring
together soil C data from diverse research networks into a harmonized
dataset that can be used for synthesis activities and model development. The
research network sources for SoDaH span different biomes and climates,
encompass multiple ecosystem types, and have collected data across a range
of spatial, temporal, and depth gradients. The rich datasets assembled in
SoDaH consist of observations from monitoring efforts and long-term
ecological experiments. The SoDaH database also incorporates related
environmental covariate data pertaining to climate, vegetation, soil
chemistry, and soil physical properties. The data are harmonized and
aggregated using open-source code that enables a scripted, repeatable
approach for soil data synthesis. Finally, to accompany SoDaH, we provide
data visualization and analysis tools that can be used to query and analyze
the aggregated database.
Database sources and structure
Research networks provide a powerful observational platform for enhancing
our understanding of ecosystems. For example, in the United States, three
research networks funded by the National Science Foundation collect soil
data that deepen understanding and improve the representation of soil
biogeochemical processes in models. These include the LTER network
(https://lternet.edu/, last access: 28 April 2021), Critical Zone Observatories and their successor
sites (CZO; http://criticalzone.org/national/, last access: 28 April 2021, and CZ Net, https://criticalzone.org/, last access: 28 April 2021), and the National Ecological
Observatory Network (NEON; https://www.neonscience.org/, last access: 28 April 2021, NEON, 2020
). Other
coordinated research activities that further expand data availability
include community efforts like the Nutrient Network (NutNet;
https://nutnet.org/, last access: 28 April 2021) and Detritus Input and Removal Treatments (DIRT;
https://dirtnet.wordpress.com/, last access: 28 April 2021). We compiled soil data from these five
research networks into the SoDaH database, version 1.0.
The unique perspectives and historical legacies of each network
synergistically offer insights into understanding many aspects of SOM
dynamics. For example, data from LTER, DIRT, and NutNet sites are generally
long-term datasets that focus on surface soil (< 30 cm) properties
across gradients and response to experimental manipulations. Data from CZO
sites tend to contribute information on soil geochemical properties and
expand focus to include deeper (> 30 cm) soil horizons. Finally,
NEON employs standardized data collection procedures that span
continental-scale ecoclimatic gradients (Fig. 1).
Conceptual diagram that summarizes the strengths and research foci
of different experimental networks contributing to SoDaH, modified from
Weintraub et al. (2019).
Diagram showing hierarchical relationship between data fields in
the Soils Data Harmonization (SoDaH) database, which includes metadata,
location, profile, and layer fields. Each data field lists a short
description of some of the variables used along with the variable name used
in the database. To facilitate data contributions these data fields were
grouped into “location” and “profile” tabs on the metadata template used by data
contributors. The right side of the figure illustrates data from two
hypothetical locations (e.g., a LTER and CZO site, respectively) where
Location 1 includes data from two profiles that each have information from
one layer. Location 2 provides data from one profile that has information
from three layers. Any location may provide data from multiple profiles or
layers. With data harmonization data for each profile and layer will inherit
metadata and location data that are provided in the location tab.
The SoDaH dataset focuses on soil organic carbon (SOC) concentration (% C), estimated SOC stocks (g C m-2), and associated covariates that may
be useful in explaining variation in SOC stocks within and among sites. To
avoid confounding the interpretation of SOC measurements collected by
different approaches (e.g., Walkley–Black and mass loss on ignition), we
focused on synthesizing SOC measurements from soil samples that were
acidified if needed to remove inorganic carbonates, then analyzed for total
C using elemental analyzer. Beyond SOC, covariates collected in SoDaH
include abiotic factors (e.g., climate (mean annual temperature and
precipitation), soil depth, bulk density, particle size distribution, and
mineralogy), vegetation characteristics (including vegetation type and above
and belowground root productivity, biomass, and chemistry), and additional
soil chemical properties (total nitrogen, phosphorus, pH, etc.).
Recognizing that the cyber landscape of soil databases is expanding
(Malhotra et al., 2019), we wanted to structure SoDaH in a manner consistent
with existing databases, perhaps most notably the International Soil Carbon
Network (ISCN; Nave et al., 2016; Harden et al., 2017), which similarly
focuses on SOC concentrations and stocks in bulk soils. The ISCN uses a
hierarchical data structure that links metadata information with fields for
location, profile, and soil layer data. We maintained the ISCN's basic
structure in SoDaH (Fig. 2), as it provides a logical means to structure
relationships between different measurements (i.e., variables). A similar
approach was also used in the International Soil Radiocarbon Database
(ISRaD; Lawrence et al., 2020), which primarily focuses on synthesis of
additional information about radiocarbon from bulk soils, soil fractions,
and soil gases. Given this focus of ISRaD, the SoDaH database contains only
sparse data on isotopes and SOM fractions. Since SoDaH and ISCN focus on SOC
measurements and have a similar structure, we hope they may be used together
in future studies.
The unique contribution from SoDaH, relative to other soil databases, is
that SoDaH is built on several network science efforts in the United States
and presents a usable, extensible database for contributing and analyzing
data. Moreover, SoDaH allows for individual locations to contribute results
from experimental manipulations, repeated measurements from long-term
studies, and local- to regional-scale gradients across ecosystems or
landscapes. Data from these kinds of studies should be incorporated into
existing database structures, like ISCN, but the additional metadata
requested as part of SoDaH help database users understand more information
about how data were collected from individual studies. Thus, SoDaH allows
for the harmonization of data spanning a greater range of spatial and
temporal scales than other databases and enables the incorporation of
ecosystems responses to manipulations, which is not a possibility for other
databases.
Given the focus on experimental manipulations, we requested additional
categorical information on location and profile fields to clarify aspects of
data collection and experimental design. This includes flags in the location
field asking if datasets include measurements that are repeated over
multiple time points, come from experimental manipulations, or represent
gradient studies. We also asked dataset contributors to identify “control”
or unmanipulated sample identifiers when necessary. We accommodated various
experimental designs and data hierarchies with fields to describe this
information, such as whether plots are grouped into blocks or watersheds,
and the organization of treatment levels, in the profile field of the
database. For example, at one site, data may be collected from plots along
an elevational transect, whereas another dataset may include information
from a nitrogen fertilization treatment that was conducted on experimental
plots in a replicated block design. Maintaining these data hierarchies is
important for database users to inform how best to aggregate data collected
from diverse networks, individual study sites, and unique experimental
designs.
The workflow for synthesizing is summarized in Fig. 3 and in the following
sections. Briefly, Primary data (Level-0) are identified by data providers
and variables are mapped to standardized units and vocabulary using the
metadata templates (Sect. 2.2). These data are harmonized into Level-1
data with soil harmonization script that renames variables, conducts unit
conversions, and performs quality control checks (Sect. 2.3). Finally,
Level-1 data are aggregated into the Level-2 dataset, which can be
visualized with the SoDaH R Shiny app and queried with data analysis tools
(Sect. 2.4).
Data identification and contributions
To begin populating the SoDaH database, we identified data contributors who
were familiar with primary datasets available from individual study sites
and research networks. These primary data may or may not be in a published
state but, if not published, would be equivalent to data provided for
publication. Many of the datasets in SoDaH were already published in public
repositories like EDI, the repository for LTER data, or available through
the NEON data portal. Users can find these primary data using the DOI
provided for individual dataset in the harmonized dataset. Other datasets
that we wanted to include in SoDaH, however, had not been published or were
difficult to find or identify (mainly data from CZO sites and the DIRT
network, but also some LTER data). Publishing these primary data remains an
active priority for our working group. Data providers who were familiar with
the diversity of datasets that are available at a study site or a network
provided expertise to link soil C datasets with appropriate ancillary data.
The SoDaH database was constructed by data contributions from individual
sites or research networks who provided flat (.csv) files to a shared
directory on Google Drive. The dataset (or datasets) from each site, study,
or network was placed in their own subdirectory along with a metadata
template that was used to map variable names in the primary (Level-0) data
to the structure of SoDaH (Fig. 3). The metadata template was developed to
facilitate data harmonization in a scripted, repeatable manner that
maintained the integrity of the primary datasets
(https://lter.github.io/som-website/database.html, last access: 28 April 2021). To simplify the workflow
for data contributors, the metadata template only includes a single tab each
for location and profile data. Within these tabs, data contributors are able
to add information on metadata (found on the “location” tab) and layer or
fraction data (found on the “profile” tab; Fig. 2). Layer data include
information on soil chemical and physical properties that may be measured on
bulk soils for defined soil horizons or depth increments. Fraction data
would include similar measurements on defined fractions within individual
soil layers (e.g., percent soil organic carbon on density fractionated
soils). Note that SoDaH currently has sparse data from measured soil fractions,
which have therefore been omitted from Fig. 2 for simplicity, but the
database structure can include information on soil fractions.
This initial step of our data harmonization still requires manual effort
from data providers, as they have to map the names of measured variables
from primary data with the appropriate variable in SoDaH. Data contributors
enter relevant metadata and site information that may not be included in the
primary datasets. They provide additional information from controlled
drop-down cells with information on units for each variable (e.g., % C, g C kg-1 soil, mg C kg-1 soil) or on methodologies used (e.g., soil P measured by Bray, Melich). In the harmonized dataset, we
convert analyte names and units to a standard output and include
methodological information (Sect. 2.3). This approach accommodates a broad
suite of soil and related variables (e.g., climate, vegetation
characteristics, ecosystem productivity). In the future, we aim to
further reduce data provider input requirements, but only if the community
converges on standardized variable names and units of measure (sensu Billings et
al., 2021). Ultimately sophisticated metadata, such as controlled
vocabularies and other, more expressive semantic technologies, may
facilitate scripted harvesting of data from disparate networks and
repositories (e.g., see review by Buck et al., 2019, for trends and examples
in marine science).
The metadata template in SoDaH matches site-level information with the
detailed measurements collected at each study site. Data on the location tab
represents site characteristics for a single site or location (e.g.,
Prospect Hill Warming experiment at Harvard Forest). Accordingly, the
harmonization script broadcasts data provided on the location tab (latitude,
longitude, mean annual temperature, etc.) to every row of the harmonized
dataset. Data on the profile tab include profile information about
experimental levels (e.g., plots within experimental blocks) and
experimental treatments (e.g., +N fertilization) that help clarify how the
data were collected. Data on the profile tab should also correspond to
columns of variables that are reported in the Level-0 data (e.g., soil
organic C measured at different soil layers). Accordingly, the harmonization
script copies each unique measurement from the profile tab into a column of
data in the harmonized dataset. Data contributors, therefore, can move
variables from the location to profile tabs when appropriate. For example,
NutNet and NEON data were submitted to SoDaH with information from multiple
sites on a single .csv file that provided information about each site as
unique columns of data. We, therefore, moved site information (e.g.,
climate, latitude and longitude) onto the profile tab for these networks.
Similarly, gradient studies that report tabular data for individual soil
profiles can move information on slope, aspect, vegetation communities, or
parent material (typically on the location tab) onto the profile tab of the
metadata template.
The harmonization script can harmonize multiple datasets from the same study
location. For example, a dataset may consist of multiple data files that
each contain details about different aspects of the study (e.g., soil data
in one file, aboveground productivity in another file); the harmonization
script will harvest all variables identified in the metadata file from the
suite of data files (as long as they are in the same Google directory as the
metadata file). However, because SoDaH is a flat database values from these
different data files will be stacked, meaning that information from
different Level-0 datasets would be recorded in different rows of the
aggregated Level-2 database (in the example above, soil properties and
productivity will be included, but in different rows). Additional
aggregation steps, therefore, may be required to align data within sites.
Users can find this information in the database column labeled
merge_align, which is a logical indicator that identifies if multiple data files can be merged. Notes under columns align_1 and align_2 are intended to help communicate what common data
fields can help with this alignment (e.g., experimental or treatment levels,
L1 and tx_L1, respectively). To help users understand the database column information, the complete database key is provided in the SoDaH online
application and gives users descriptions of the column contents.
Data harmonization and aggregation
We developed the soilHarmonization package in R (R Core Team, 2020) to harmonize and aggregate
the SoDaH database. The soilHarmonization package is publicly available (https://github.com/lter/soilHarmonization, last access: 28 April 2021). The package includes functions
that harmonize Level-0 data into Level-1 data. Data contributors or database
managers use the data_harmonization function tools to read and harmonize user-provided primary
data that are mapped to a metadata template with controlled vocabulary and
standard units (Fig. 3). Users point to the Google Drive directory where
Level-0 data are located (primary data and metadata template), and the
data_harmonization function generates a new flat file(s) in which the variable names and units
are standardized in the output (Level-1 data). The harmonized dataset
includes unique columns of data from those defined in the profile tab as
well as columns of data with site-level information from the location tab.
The package also includes a suite of QC tools that confirm proper data type
(e.g., strings are not interspersed with numeric values) and that numeric
data, once converted to appropriate units, fall within an expected range. A
summary of inputs, outputs, harmonization steps, and a QC report are
detailed in an accompanying document (.pdf) for each harmonized dataset.
These Level-1 data products are stored in the same Google Drive directory as
the Level-0 data with resulting output identified with a modified filename.
This allows data contributors and database managers to verify the QC report
and ensure appropriate data harmonization.
Illustration of the SoDaH workflow and data levels. Primary data
(Level-0) are identified by data providers, and variables are mapped to
standardized units and vocabulary using the metadata templates. These data
are harmonized into Level-1 data with soil harmonization script that renames
variables, conducts unit conversions, and performs quality control checks.
Finally, Level-1 data are aggregated into the Level-2 dataset, which can be
visualized with the SoDaH R Shiny app and queried with data analysis tools.
After generating Level-1 data from all Level-0 data, we combined harmonized
data files into an aggregated dataset (.rds or .csv format; Fig. 3). This
dataHarvest function is intended for use by database managers and is available on the LTER SOM GitHub page (https://github.com/lter/lterwg-som/tree/main/data-aggregation/, last
access: 22 December 2020). This function aligns columns of Level-1 data into a single, Level-2, dataset. The resulting SoDaH database (version 1.0) we
describe here is a single, flat dataset that has columns corresponding to
variables in the metadata template and rows for each measurement.
Data visualization and analysis
To facilitate user interaction with the SoDaH database, and to provide a
simplified approach for data queries and analysis, we developed a web-based
application using R Shiny (Chang et al., 2020). This SoDaH application is
publicly accessible and hosted by the National Center for Ecological
Analysis and Synthesis (NCEAS) at https://cosima.nceas.ucsb.edu/lter-som
(last access: 22 December 2020; source code:
https://github.com/lter/lterwg-som-shiny, last access: 28 April 2021). With the SoDaH application, users
can perform a number of tasks to aid data discovery, visualization, and
analysis. We provide a brief description of this resource that highlights
key features of the R Shiny SoDaH application.
In the “query” section of the application, the top portion of the page provides a
variety of data filter options to assist users with partitioning the
database. Specifically, users may subset the database by any combination of
research network, experiment type, and soil depth while also specifying
whether they wish to include or exclude experimental treatments or
time-series data. Below the filter options, the “output” section of the page
contains three separate features arranged into labeled application tabs. The
“plot” tab allows users to quickly create basic analysis plots (point, histogram,
or boxplot) using both covariates (e.g., Fe concentration) and metadata
(e.g., mean annual precipitation). In the “map” tab, users may specify which
analyte in the database to display on a spatial map. Numeric values are
symbolized using a color gradient and the interactive map functionality
allows users to both adjust the map scale and select from numerous basemap
options. Finally, the “table” tab provides users with the ability to directly view,
search, and download the user-specified data subset as a flat file (.csv).
The plot, map, and table features are all responsive to user-specified
changes in the data filters and will update in real time.
The data table on the “query” page of the SoDaH Shiny application is responsive to
the filter options at the top of the “query” page. When users click the “Download
data” button next to the table, the downloaded .csv file will contain the
same data shown in the application table at that time. Code examples for
working with the database, including how to filter by specific column
values, are provided in the GitHub repository
(https://github.com/lter/lterwg-som/tree/main/data-processing, last access: 28 April 2021).
In the “data summary” section of the SoDaH application, two feature tabs are provided to help users identify the data available for a specific site or analyte. The “by analytes” tab allows users to view the number of analyte values that exist across all of the unique sites in the database. Users may specify up to four different analytes at a time to be included in the summary table output. The “by site” tab allows users to view all of the analyte data available for a specific site.
As the number of data may be quite large for some sites, options are
provided to narrow the summary output to include only profile, location, or
character class data.
The SoDaH application also includes a “data key” section, where users may view a full
copy of the metadata template used for the SoDaH database construction,
including descriptions of database fields and their associated metadata. The
searchable key is split into two sections, location and profile, in the same
manner as the metadata template used to describe primary data for the
harmonization process. Field names in the provided key match exactly with
analyte and metadata options provided in the “plot” and “map” features in the “query” section
of the application. Finally, the application provides a “comments” section where users
may submit an inquiry about the database or the application.
For users seeking to move beyond the functionality provided by the SoDaH
application, R scripts are provided through the LTER SOM GitHub repository
(https://github.com/lter/lterwg-som/tree/main/data-projects, last access:
22 December 2020) to facilitate and demonstrate scripting language to import,
filter, summarize, and map data from the SoDaH database. This repository is
intended to facilitate use of the SoDaH database, and the scripts used to
generate figures in this paper are available in the repository. We encourage
database users to draw from these existing resources and contribute new
scripts they develop for scientific analysis of data in SoDaH.
Additional data aggregation steps may be required to fully realize strengths
of the SoDaH database. These could include identifying suitable approaches
to aggregate and aligning data within sites. The aggregation steps
currently implemented in SoDaH may not be appropriate for particular
research questions, especially those concerning spatial and temporal
gradients. Therefore, users may need to align rows of data that are from the same
profile or location but were harvested from multiple data files. Currently these data are found in different rows that are being stacked within the flat database. For example, a site
may contribute data on soil chemical properties, soil physical properties,
microbial stoichiometry and biomass, litterfall chemistry, and litterfall
fluxes with each as an independent dataset. Moreover, these variables may be
measured multiple times during a long-term study but not necessarily at the
same time or at the same frequency. Finally, information from a single site
may include a gradient study across a hillslope, chronosequence, or region
that may influence how data users want to aggregate individual measurements.
The SoDaH metadata template prompts data providers to indicate if data from
multiple files need to be aligned and, if so, the grouping variable(s) that
can be used to join this information (see Sect. 2.2). The template also
prompts data providers to indicate if datasets include time-series data or
data from a gradient study. Users of SoDaH are encouraged to consider this
information in their analyses.
Database descriptionSpatial and temporal distributions
The SoDaH database currently contains data from 215 locations and 186 unique
study sites, with data contributed from DIRT, NutNet, LTER, NEON, and CZO
networks. There are more locations than study sites in the database because
some sites contributed datasets from multiple locations or experiments. The
flat database contains 160 columns of variables and nearly 300 000 rows of
information, but it is relatively sparsely populated, with 13.9 million
non-missing observations (roughly 30 % of the database). Given the focus
on NSF-funded research networks and observatories, most of the measurements
are taken from the United States, but NutNet and DIRT networks include a
number of international study sites (Fig. 4).
Spatial distribution of study locations representing five
research networks in SoDaH globally and in the contiguous USA.
Mean annual temperature from all locations was 10.1 ± 7.1 ∘C
(mean ± 1σ, n=212) with a range of -12 to 27.2 ∘C. Mean
annual precipitation from all locations was 904 ± 638 mm yr-1 (n=213), with a range of 105 to 4250 mm yr-1. Land cover classifications include urban, cultivated, rangeland/grasslands, shrublands, and forests, but land cover is reported only for a subset (n=87) of the study locations.
We briefly review characteristics of data contributed from the five networks
represented in SoDaH (Fig. 5). The CZO generally has a focus on making
one-time characterizations that extend deeper in soil and regolith profiles
than other networks. Data from DIRT span relatively few sites and only
include surface soil layers but provide repeated measurements and their
response to experimental manipulations. The LTER network provides data from
comparatively few study sites, but LTER sites have longer measurement
records than other networks in SoDaH given the network's 40-year history.
Some data from LTER sites also include measurements to ∼ 1 m
depth. By design, NEON provides data with broad geographic coverage and
samples both surface and deeper soil horizons. The current temporal record
from NEON sites is relatively short, but it is expected to extend for the next
30 years. Finally, NutNet provides the greatest number and largest spatial
distribution of sites, all from grassland ecosystems with sampling depths
from 0 to 10 cm.
Temporal coverage and depth of measurements taken from
different study sites and grouped by research network. Our intent with this
figure is to illustrate the number of sites in each network, the temporal
length of their data record, and the depth to which soils are typically
sampled.
Summary of the networks and number of sites contributing data from
experimental manipulations, gradient studies, and time series of repeated
measurements. Gradient studies may include measurements along a hillslope
catena (e.g., several CZO sites), across vegetation communities (typically
LTER sites), or surveys intended to capture local to regional variability
(especially NEON periodic soil sampling). Time series studies involve
repeated measurements in the same sites over time (LTER and NEON) and they
which may also include experimental manipulations (e.g., NutNet, DIRT, and
LTER).
Experimental manipulationNetworks (site)Nutrient additionsNutNet (109) LTER (5)Litter manipulationsDIRT (6)Agricultural managementLTER (3)Forest harvestLTER (2) CZO (1)WarmingLTER (2)FireLTER (2)Precipitation manipulationLTER (2) CZO(1)Elevated CO2LTER (1)Other (mostly related to management, disturbance, or land use history)NutNet(109) LTER (10) CZO (1)Gradient studiesNEON (47) LTER (11) CZO (7)Time seriesNutNet(109)1 NEON (35)2 LTER (10) DIRT (5)
1 Repeated measurements for NutNet are for plant
productivity, not soil measurements.
2 Not all NEON sites have been sampled more than once per dataset.
Experimental manipulations, gradients, and time series
SoDaH is unique in the landscape of soil databases because it includes data
from both experimental manipulations (at 132 sites) and gradient studies and
includes time series of soil data. Nutrient manipulations from NutNet make
up the majority (109) of experimental manipulations. All experimental
manipulations in SoDaH are summarized in Table 1 and include manipulations
from all 15 LTER sites for which we have data, 6 DIRT sites and 1
CZO site. The database also includes gradient studies from 66 sites (with
data from NEON, CZO, and LTER networks) and time series data from 158 sites
(with data from NutNet, NEON, LTER, and DIRT networks, Table 1).
Database use and analyses
Aggregating data in SoDaH presents challenges in how to most appropriately
group multiple measurements taken from individual study locations that
include diverse sampling protocols, unique experimental designs, and
measurements from multiple soil depths. Moreover, particular locations may
include manipulative experiments, gradient studies, and time series of
repeated measurements. The appropriate aggregation of SoDaH requires users
to become familiar with data structures of the database to address
particular scientific questions. For this reason, we see the R Shiny web app
as an invaluable tool for querying the data available from SoDaH. As
mentioned in Sect. 2.4, future contributions of code to analyze the SoDaH
database are encouraged. These contributions should be made to the LTER SOM
GitHub repository, with a priority on developing additional utilities to
align and aggregate datasets from individual sites and locations.
Contributions will be reviewed by the SoDaH steering committee (currently
William R. Wieder, Derek Pierson, and Stevan Earl) and made publicly available. The committee will
continue oversight while new funding options and/or partnerships (e.g.,
ISCN) are explored.
Database contributions and database versioning
We built the SoDaH tools to help facilitate the harmonization of diverse
soil datasets that focus on soil C. To that end, we welcome
contributions of new data from new sites that may be part of the research
networks presented here, additional research networks (e.g., Ameriflux
https://ameriflux.lbl.gov/, last access: 28 April 2021, Drought-Net
https://wp.natsci.colostate.edu/droughtnet/, last access: 28 April 2021, Long-Term Agroecosystem
Research https://ltar.ars.usda.gov, last access: 28 April 2021, Africa Soil Information Service (AfSIS)
http://africasoils.net/services/data/, last access: 28 April 2021, European LTER networks
https://www.lter-europe.net/, last access: 28 April 2021, or others), as well as data from sites that
are unaffiliated with a research network. The SoDaH website
(https://lter.github.io/som-website/database.html, last access: 28 April 2021) contains more information on how to contribute data. Briefly, data
contributors need to place primary datasets and a completed copy of the
SoDaH metadata template into a shared Google Drive folder and notify the
SoDaH editor (soildataharmonization@gmail.com) that their data are ready for
ingestion into SoDaH. These data contributions will also be reviewed by the
SoDaH steering committee. We ask that new contributions of primary data that
are harmonized into SoDaH be published with a unique DOI.
Updated releases of SoDaH will be made periodically after a threshold number
of new contributions have been made to the database, in light of any changes
to the database structure, or if any errors are detected and corrected.
Versions are tracked with a version number in the form of “major.minor.”
in addition to the date of publication. Each version of the dataset will
receive a unique citation and DOI through the EDI data portal for users to
reference.
Data availability and user guidelines
The SoDaH v1.0 database and some exemplary analyses are hosted in the EDI
repository (Wieder et al., 2020;
https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4). We encourage users of SoDaH data to cite both this publication and
the dataset citation provided by the EDI data portal in their products.
Author contributions
WRW and KL received funding for the synthesis.
WRW, SE, and DP designed the approach harmonized datasets and published the
synthesis. All other authors contributed data to the synthesis and provided
input on this article.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This paper stems from the synthesis group “Advancing Soil Organic Matter
Research: Synthesizing Multi-scale Observations” supported by the
Long-Term Ecological Research Network Office and the National Center for Ecological Analysis and Synthesis,
UCSB, lead by Kate Lajtha and William R. Wieder. William R. Wieder was also supported by the Niwot Ridge LTER
program, Stevan Earl by the Central Arizona–Phoenix LTER program to Kate Lajtha, and to the H. J. Andrews LTER program.
Financial support
This research has been supported by the National Science Foundation, Directorate for Biological Sciences (grant nos. 1545288, 1929393, 1637686, 1832016, 1257032, and 1440409).
Review statement
This paper was edited by Sibylle K. Hassler and reviewed by Caitlin Pries and Jeffrey Beem Miller.
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