SWMPr: An R Package for Retrieving, Organizing, and Analyzing Environmental Data for Estuaries
Marcus W Beck
US Environmental Protection Agency, National Health and Environmental Effects Research Laboratory2016-04-03
Abstract
The System-Wide Monitoring Program (SWMP) was implemented in 1995 by the US National Estuarine Research Reserve System. This program has provided two decades of continuous monitoring data at over 140 fixed stations in 28 estuaries. However, the increasing quantity of data provided by the monitoring network has complicated broad-scale comparisons between systems and, in some cases, prevented simple trend analysis of water quality parameters at individual sites. This article describes the SWMPr package that provides several functions that facilitate data retrieval, organization, and analysis of time series data in the reserve estuaries. Previously unavailable functions for estuaries are also provided to estimate rates of ecosystem metabolism using the open-water method. The SWMPr package has facilitated a cross-reserve comparison of water quality trends and links quantitative information with analysis tools that have use for more generic applications to environmental time series.Introduction
The development of low-cost, automated sensors that collect data in near real time has enabled a proliferation of standardized environmental monitoring programs (Glasgow et al. 2004; Fries et al. 2008). An invaluable source of monitoring data for coastal regions in the United States is provided by the National Estuarine Research Reserve System (NERRS, http://www.nerrs.noaa.gov/). This network of 28 estuary reserves was created to address long-term research, monitoring, education, and stewardship goals in support of coastal management. The System-Wide Monitoring Program (SWMP) was implemented in 1995 at over 140 stations across the reserves to provide a robust, long-term monitoring system for water quality, weather, and land-use/habitat change. Environmental researchers have expressed a need for quantitative analysis tools to evaluate trends in water quality time series given the quantity and quality of data provided by SWMP (System-Wide Monitoring Program Data Analysis Training 2014).
This article describes the SWMPr package that was developed for estuary monitoring data from the SWMP. Functions provided by SWMPr address many common issues working with large datasets created from automated sensor networks, such as data pre-processing to remove unwanted information, combining data from different sources, and exploratory analyses to identify parameters of interest. Additionally, web applications derived from SWMPr and shiny illustrate potential applications using the functions in this package. The software is provided specifically for use with NERRS data, although many of the applications are relevant for addressing common challenges working with large environmental datasets.
Overview of the SWMP network
The SWMPr package was developed for the continuous abiotic monitoring network that represents a majority of SWMP data and, consequently, the most challenging to evaluate. Abiotic elements monitored at each reserve include water quality (water temperature, specific conductivity, salinity, dissolved oxygen concentration, dissolved oxygen saturation, depth, pH, turbidity, chlorophyll fluorescence), weather (air temperature, relative humidity, barometric pressure, wind speed, wind direction, photosynthetically active radiation, precipitation), and nutrient data (orthophosphate, ammonium, nitrite, nitrate, nitrite + nitrate, chlorophyll a). Each of the 28 estuary reserves has no fewer than four water quality stations and one weather station at fixed locations. Water quality and weather data are collected at 15 minute intervals, whereas nutrient data are collected monthly at each water quality station. Data are made available through the Centralized Data Management Office (CDMO) web portal (http://cdmo.baruch.sc.edu/), where quality assurance/quality control (QAQC) measures are used to screen the information for accuracy and reliability. The final data include timestamped observations with relevant QAQC flags.
At the time of writing, the CDMO web portal provides over 60 million
water quality, weather, and nutrient records that have been
authenticated through systematic QAQC procedures. Records for each
station are identified by a seven or eight character name that specifies
the reserve, station, and parameter type. For example, ‘apaebwq’ is the
water quality identifier (‘wq’) for the East Bay station (‘eb’) at the
Apalachicola reserve (‘apa’). Similarly, a suffix of ‘met’ or ‘nut’
specifies the weather (meteorological) or nutrient stations. All reserve
names, stations, and date ranges for each parameter type can be viewed
on the CDMO website. Alternatively, the site_codes (all
sites) or site_codes_ind (single site) functions provided
by SWMPr can be used. As noted below, an IP address must be
registered with CDMO before using the data retrieval functions in
SWMPr. Web services are provided by CDMO for direct access to
SWMP data through http requests, in addition to standard graphical user
interface options for selecting data. The data retrieval functions in
SWMPr are simple calls to the existing retrieval functions on
CDMO web services, as explained below.
Structure of the SWMPr package
SWMPr functions are categorized by one of three steps in the
data workflow: retrieving,
organizing, and
analyzing. Functions for retrieving are used
to import the data into R as a "swmpr" object class.
Functions for organizing and analyzing the data provide methods for
working with a "swmpr" object. The following describes the
package structure, beginning with the retrieval functions, a description
of the "swmpr" object returned after retrieval, and,
finally, the organizing and analyzing functions.
Data retrieval
SWMPr can import data into R through direct download from
the CDMO or by importing local data that was previously downloaded
(Table 1). The IP address for the computer
making the request must be registered if the first approach is used (see
CDMO website). The
site_codes or site_codes_ind functions can be
used to view site metadata.
| Function | Description |
|---|---|
all_params |
Retrieve records starting with the most recent at a
given station, all parameters. Wrapper to
exportAllParamsXMLNew function on web services. |
all_params_dtrng |
Retrieve records of all parameters within a given date
range for a station. Optional argument for a single parameter. Wrapper
to exportAllParamsDateRangeXMLNew. |
import_local |
Import files from a local path. The files must be in a specific format, such as those returned from the CDMO using the zip downloads option. |
single_param |
Retrieve records for a single parameter starting with
the most recent at a given station. Wrapper to
exportSingleParamXMLNew function on web services. |
site_codes |
Get metadata for all stations. Wrapper to
exportStationCodesXMLNew function on web services. |
site_codes_ind |
Get metadata for all stations at a single site. Wrapper
to NERRFilterStationCodesXMLNew function on web
services. |
# retrieve metadata for all sites
site_codes()
# retrieve metadata for a single site
site_codes_ind('apa')Retrieval functions to import data directly into R from the CDMO
include all_params, all_params_dtrng, and
single_param. Due to rate limitations on the CDMO server,
the retrieval functions return a limited number of records with each
request. However, the SWMPr functions use the native CDMO web
services iteratively (i.e., within a loop) to obtain all requested
records. Download time can be excessive for longer time series.
# all parameters for a station, most recent
all_params('hudscwq')
# get all parameters within a date range
all_params_dtrng('hudscwq', dtrng = c('09/01/2013', '10/01/2013'))
# get single parameter within a date range
all_params_dtrng('hudscwq', dtrng = c('09/01/2013', '10/01/2013'),
param = 'do_mgl')
# single parameter for a station, most recent
single_param('hudscwq', param = 'do_mgl')The second approach for data retrieval is to use the
import_local function to import data into R after
downloading from CDMO. This approach is most appropriate for large data
requests. The import_local function is designed for data
from the zip
downloads feature in the advanced query section of the CDMO website.
The zip downloads feature can be used to obtain a large number of
records from multiple stations in one request. The downloaded data will
be in a compressed folder that includes multiple .csv files by year for
a given data type (e.g., apacpwq2002.csv, apacpwq2003.csv,
apacpnut2002.csv, etc.). The import_local function can be
used to import files directly from the zipped folder.
The "swmpr" object class
All data retrieval functions return a "swmpr" object
that includes relevant data and several attributes describing the
dataset. The data include a datetimestamp column in the
timezone for a station and additional parameters for the data type
(weather, nutrients, or water quality). Corresponding QAQC columns for
each parameter are also returned if provided by the initial data
request. The following shows an example of the raw data imported using
all_params.
# import all paramaters for the station
# three most recent records
exdat <- all_params('apadbwq', Max = 3, trace = F)
exdat
## datetimestamp temp f_temp spcond f_spcond sal f_sal do_pct
## 1 2015-11-03 11:15:00 26 0 45 0 29 0 78
## 2 2015-11-03 11:30:00 26 0 46 0 30 0 76
## 3 2015-11-03 11:45:00 26 0 46 0 30 0 75
## f_do_pct do_mgl f_do_mgl depth f_depth ph f_ph turb f_turb chlfluor
## 1 0 5 0 2 0 8 0 2 0 NA
## 2 0 5 0 2 0 8 0 5 0 NA
## 3 0 5 0 2 0 8 0 5 0 NA
## f_chlfluor level f_level cdepth clevel f_cdepth f_clevel
## 1 -2 NA -1 2 NA 3
## 2 -2 NA -1 2 NA 3
## 3 -2 NA -1 2 NA 3The attributes of a "swmpr" object are descriptors that
are appended to the raw data (Table 2).
These act as metadata that are used internally by many of the package
functions and are updated as the data are processed. The attributes are
not visible with the raw data but can be viewed as follows.
# import sample data from package
data(apadbwq)
dat <- apadbwq
# view all attributes of dat
attributes(dat)
# view a single attribute of dat
attr(dat, 'station')| Attributes | Class | Description |
|---|---|---|
names |
character | Column names of the entire data set, inherited from the
data.frame object class. |
row.names |
integer | Row names of the data set, inherited from the
data.frame object class. |
class |
character | Class of the data object indicating
"swmpr" and "data.frame". |
station |
character | Station identifier used by NERRS as a string with 7 or 8 characters. |
parameters |
character | Character vector of column names for data parameters,
e.g., ’do_mgl’, ’turb’, etc. |
qaqc_cols |
logical | Indicates if QAQC columns are present in the raw data. |
date_rng |
POSIXct | Start and end dates for the data. |
timezone |
character | Timezone of the station using the city/country formata. |
stamp_class |
character | Class of the datetimestamp column, usually
"POSIXct" unless data have been aggregated. |
aTime zones that do not observe daylight savings are used
for "swmpr" objects and may not be cities in the United
States. For example, America/Jamaica is used for Eastern
Standard Time.
The "swmpr" object class was created for use with the
organizing and analyzing functions. This uses the standard S3 object
class system for R, such that specific methods for generic functions are
developed for the object class. A "swmpr" object also
secondarily inherits methods from the "data.frame" class.
Available methods for the "swmpr" class are described below
and can also be viewed:
# view available methods for swmpr class
methods(class = 'swmpr')Data organizing
The organize functions are used to ‘clean’ or prepare the imported data for analysis, including viewing and removal of QAQC flags, subsetting, combining replicate nutrient observations, creating a standardized time series, and combining data of different types (Table 3).
The qaqc function is a simple screen to retain
observations from the data with specified QAQC flags (see http://cdmo.baruch.sc.edu/data/qaqc.cfm). Each parameter
in the imported "swmpr" object will have a corresponding
QAQC column of the same name with the added prefix f_
(e.g., f_do_mgl for do_mgl). Values in the
QAQC column range from -5 to 5 to indicate the QAQC flag that was
assigned by CDMO during initial processing. The qaqc
function is used to remove observations in the raw data with given
flags, with the default option to retain only values with the
0 QAQC flag (i.e., passed initial CDMO checks).
Additionally, simple filters are used to remove obviously bad values
(e.g., wind speed values less than zero or pH values greater than 12).
Erroneous data entered as -99 are also removed. The function returns the
original data with the QAQC columns removed and NA (not
available) values for observations that do not meet the criteria
specified in the function call.
| Function | Description |
|---|---|
comb |
Combines "swmpr" objects to a common time
series using setstep, such as combining the weather, nutrients, and
water quality data for a single station. |
qaqc |
Remove QAQC columns and remove data based on QAQC flag
values for a "swmpr" object. |
qaqcchk |
View a summary of the number of observations in a
"swmpr" object that are assigned to each QAQC flag used by
CDMO. |
rem_reps |
Remove replicate nutrient data that occur on the same day. The default is to average replicates. |
setstep |
Format data from a "swmpr" object to a
continuous time series at a given timestep. |
subset |
Subset by dates and/or columns for a
"swmpr" object. This is a method passed to the generic
subset function in the base installation. |
# qaqc screen for a swmpr object, retain only '0'
qaqc(dat)
# retain all data regardless of flag
qaqc(dat, qaqc_keep = NULL)
# retain only '0' and '-1' flags
qaqc(dat, qaqc_keep = c(0, -1))SWMP data often contain observations above or below the detection
limit for the sensor or laboratory method used to quantify the
parameters. For example, nutrient data exceeding the high sensor range
are assigned a QAQC flag of -5, whereas data below the low sensor range
are assigned a QAQC flag of -4. The presence of censored data is
non-trivial and can influence the types of analyses that are appropriate
for a time series (Helsel 2012). A
detailed discussion of methods for evaluating censored data is beyond
the scope of the manuscript and existing methods for R are provided by
other packages (e.g., cents,
McLeod et al. 2014). However, the functions in SWMPr can
be used to identify censored data based on the appropriate QAQC flag for
a parameter. Viewing this information can be helpful for determining how
to further process the data with the qaqc function or
alternative methods outside of SWMPr. The qaqcchk
function returns a data.frame of the number of observations
for a parameter that is assigned to all QAQC flags, including those for
censored data. SWMP data should not be analyzed without viewing this
information to determine an appropriate method to address data with
questionable QAQC flags.
# view the number of observations in each QAQC flag
qaqcchk(dat)A subset method added to the existing generic subset
function in R is available for "swmpr" objects. This
function is used to subset the data by date and/or a selected parameter.
The date can be a single value or as two dates to select records within
the range. The former case requires a binary operator as a character
string passed to the operator argument, such as
’>’ or ’<=’. The subset argument for the
date(s) must also be a character string of the format YYYY-mm-dd HH:MM
for each element (e.g., ’2007-01-01 06:30’).
# import data
data(apaebmet)
dat <- apaebmet
# select two parameters from dat
subset(dat, select = c('rh', 'bp'))
# subset records greater than or equal to a date
subset(dat, subset = '2013-01-01 0:00', operator = '>=')
# subset records within a date range, select two parameters
subset(dat, subset = c('2012-07-01 6:00', '2012-08-01 18:15'),
select = c('atemp', 'totsorad'))The setstep function formats a "swmpr"
object to a continuous time series at a given time step. The function
also has a default method making it useful for standardizing arbitrary
time series to a given interval. The first argument of the function,
timestep, specifies the desired time step in minutes
starting from the nearest hour of the first observation. The second
argument, differ, specifies the allowable tolerance in
minutes for matching existing observations to the defined time steps in
cases where the two are dissimilar. Values for differ that
are greater than one half of the value of timestep are not
allowed to prevent duplication of existing data. Likewise, the default
value for differ is one half of the time step.
# import, qaqc removal
data(apadbwq)
dat <- qaqc(apadbwq)
# convert time series to two hour invervals
# tolerance of +/- 30 minutes for matching existing data
setstep(dat, timestep = 120, differ = 30)The comb function is used to combine multiple
"swmpr" objects into a single object with a continuous time
series at a given step. The setstep function is used
internally such that timestep and differ are
accepted arguments for comb. Data are combined by creating
a master time series that is used to iteratively merge all
"swmpr" objects. The time series for merging depends on the
value passed to the method argument. Passing
’union’ to method will create a time series
that is continuous from the earliest and latest dates for all input
objects, whereas ’intersect’ will create a continuous time
series from the set of dates that are shared between input objects. A
character string or numeric vector can also be used to specify which of
the input objects to use as the master time series for combining. As
with setstep, a default method for comb is
provided to allow use with arbitrary data structures. Both functions
treat missing data as NA values, either for observations
that exceed the allowable tolerance for the differ argument
of setstep or for portions of time series that do not
overlap given the method argument passed to
comb.
# get nut, wq, and met data as separate objects
data(apacpnut)
data(apacpwq)
data(apaebmet)
swmp1 <- apacpnut
swmp2 <- apacpwq
swmp3 <- apaebmet
# combine nut and wq data by union
comb(swmp1, swmp2, method = 'union')
# combine nut and met data by intersect
comb(swmp1, swmp3, method = 'intersect')
# combine nut, wq, and met data by nut time series, two hour time step
comb(swmp1, swmp2, swmp3, timestep = 120, method = 'apacpnut')Data analysis
The analysis functions range from general purpose tools for time
series analysis to more specific functions for working with continuous
monitoring data in estuaries (Table 4). The
general purpose tools are "swmpr" methods for existing S3
generics or are slight modifications to existing functions. These
include aggreswmp to combine observations by set periods of
time (e.g., weeks, months), smoother to average time series
with a moving window, and approx to substitute missing data
with interpolated values. For brevity, the general functions are not
discussed. More specific functions for environmental time series include
decomposition functions, decomp and decomp_cj,
and functions to estimate and plot ecosystem metabolism from combined
water quality and weather data. Several plotting methods to facilitate
analysis are also descibed below.
| Function | Description |
|---|---|
aggreswmp |
Aggregate "swmpr" objects for different
time periods - years, quarters, months, weeks, days, or hours. The
aggregation function defaults to the mean. |
aggremetab |
Aggregate metabolism data from a "swmpr"
object. This is primarly used within plot_metab but may be
useful for simple summaries of daily metabolism data. |
ecometab |
Estimate ecosystem metabolism for a combined water quality and weather dataset using the open-water method (Odum 1956). |
decomp |
Decompose a "swmpr" time series into
trend, seasonal, and residual components. This is a simple wrapper to
decompose (Kendall and Stuart
1983). Decomposition of monthly or daily trends is possible. |
decomp_cj |
Decompose a "swmpr" time series into
grandmean, annual, seasonal, and events components. This is a simple
wrapper to decompTs in the wq package
(Jassby and Cloern 2014). Only monthly
decomposition is possible. |
hist |
Plot a histogram for a single variable. |
lines |
Add lines to an existing plot created with
plot. |
map_reserve |
Create a map of all stations in a reserve using the ggmap package (Kahle and Wickham 2013). |
na.approx |
Linearly interpolate missing data (NA
values) in a "swmpr" object. |
overplot |
Plot multiple time series in a "swmpr"
object on the same y-axis. |
plot |
Plot a univariate time series for a
"swmpr" object. |
plot_metab |
Plot ecosystem metabolism estimates after running
ecometab on a combined "swmpr" object. |
plot_summary |
Create summary plots of seasonal/annual trends and anomalies for a single parameter. |
smoother |
Smooth "swmpr" objects with a moving
window average, passed to filter. |
The disaggregation of time series into additive or multiplicative
components is a common application for trend analysis. The
decomp function is a simple wrapper to
decompose (Kendall and Stuart
1983) that separates a time series into a trend, cyclical
variation (e.g., daily or annual), and the remainder (Figure 1). An additive decomposition assumes that
the cyclical component of the time series is stationary (i.e., the
variance is constant); otherwise, a multiplicative decomposition can be
used. The frequency argument describes the periodicity of
the cyclical parameter in units of the native time step. For example,
the frequency for a parameter with daily periodicity would
be 96 if the time step is 15 minutes (24 hours * 60 minutes / 15
minutes). For simplicity, character strings of ’daily’ or
’annual’ can be supplied in place of numeric values,
although any number can be used to identify an arbitrary cyclical
component. A starting value of the time series must be supplied in the
latter case that indicates the sequence in the cycle for the first
observation (see ts
for details).
# get data
data(apadbwq)
dat <- apadbwq
# subset for daily decomposition
dat <- subset(dat, subset = c('2013-07-01 00:00', '2013-07-31 00:00'))
# daily decomposition of DO and plot
dc_dat <- decomp(dat, param = 'do_mgl', frequency = 'daily')
plot(dc_dat)
decomp
function.
An alternative approach for decomposition is provided by the
decomp_cj function, which is a simple wrapper to the
decompTs function in the wq package (Cloern and Jassby 2010; Jassby and Cloern
2014). The decomp_cj function is a monthly
decomposition for characterizing relatively long-term trends. This
approach works best for nutrient data that are typically obtained on a
monthly cycle. The time series is decomposed into the grandmean, annual,
seasonal, and events components (Figure 2), as compared to trend, seasonal, and
random components for the decomp function above. For both
functions, the random or events components can be considered anomalies
that do not follow the trends in the remaining categories. Additional
arguments passed to decompTs can be used with
decomp_cj, such as startyr,
endyr, and type. Values passed to
type are mult (default) or add,
referring to multiplicative or additive decomposition.
# get data
data(apacpnut)
dat <- apacpnut
dat <- qaqc(dat, qaqc_keep = NULL)
# decomposition of chl
decomp_cj(dat, param = 'chla_n')
decomp_cj function.
Estimates of ecosystem metabolism provide a measure of system
productivity to evaluate whether an ecosystem is a net source or sink of
organic material. The open-water method (Odum
1956) is a common approach to quantify metabolism using a mass
balance equation that describes the change in dissolved oxygen over time
from the balance between photosynthetic and respiration processes,
corrected using an empirically constrained air-sea gas diffusion model
(Ro and Hunt 2006; Thébault et al. 2008).
A detailed discussion of the method is beyond the scope of this article,
although users are encouraged to consult references herein for
additional information (see Kemp and Testa (2012;
Needoba, Peterson, and Johnson 2012; Caffrey et al. 2013), also
the package help files). Methods for estuaries have not previously been
available in R, although the StreamMetabolism
package provides an approach for freshwater systems. The following is an
example that shows use of ecometab with a combined water
quality and weather data set. Monthly aggregations of the raw, daily
estimates are plotted using plot_metab (Figure 3).
## import water quality and weather data
data(apadbwq)
data(apaebmet)
## qaqc, combine
wq <- qaqc(apadbwq)
met <- qaqc(apaebmet)
dat <- comb(wq, met)
## estimate metabolism
res <- ecometab(dat, trace = FALSE)
plot_metab(res)
Exploratory graphics are also useful for evaluating general trends in
observed data. Several graphics showing seasonal and annual trends for a
single SWMP parameter can be obtained using the
plot_summary function (Figure 4). The plots include monthly distributions,
monthly anomalies, and annual anomalies in multiple formats. An
interactive shiny web application (Chang
et al. 2015) that uses this function is available for viewing
results for all SWMP sites (see the section Applications using the SWMPr package).
## import data
data(apacpnut)
dat <- qaqc(apacpnut)
## plot
plot_summary(dat, param = 'chla_n', years = c(2007, 2013))
plot_summary function. Summaries include monthly
distributions (means on top left, quantiles on bottom left), monthly
histograms (center), monthly means by year (top right), deviation from
monthly means (middle right), and annual trends as deviations from the
grand mean (bottom right)
Similarly, the overplot function provides an alternative
approach to viewing observed data from the same station. This function
uses the base graphics package to plot multiple time series on
the same y-axis (Figure 5).
## import data
data(apacpwq)
dat <- qaqc(apacpwq)
## plot
overplot(dat, select = c('depth', 'do_mgl', 'ph', 'turb'),
subset = c('2013-01-01 0:0', '2013-02-01 0:0'), lwd = 2)
overplot function plots multiple variables on
the same y-axis.
Finally, the map_reserve function can be used to create
a map of stations at a reserve using the ggmap package
(Figure 6, Kahle and
Wickham (2013)). The function uses Google maps of four types that
can be set with the map_type argument: terrain (default),
satellite, roadmap, or hybrid. The zoom argument can be
chosen through trial and error depending on the spatial extent of the
reserve.
# plot the stations at Jacques Cousteau reserve
map_reserve('jac')
map_reserve function.
Applications using the SWMPr package
Two shiny web applications illustrate the improved ability
to synthesize and evaluate multi-year time series using SWMPr.
The first application evaluates trends in SWMP data within and between
sites using an interactive leaflet map (Cheng and Xie (2015), Figure 7): https://beckmw.shinyapps.io/swmp_comp. Trends between
reserves can be viewed using the map, whereas trends at individual sites
can be viewed by clicking on a map location. Site-level trends are shown
below the map with a simple linear regression to show an increase or
decrease in values over time, whereas trends between sites are shown on
the map for each station as circles that identify the direction and
significance of the trend. More robust methods for evaluating trends are
currently not provided by the application and the use of simple linear
regression is meant for initial exploratory analysis. The second
application provides graphical summaries of water quality, weather, or
nutrient station data at individual stations using the
plot_summary function: https://beckmw.shinyapps.io/swmp_summary/. The output is
identical to Figure 4 with the addition of
drop down menus to select the station, date range, and parameter for
plotting.
Conclusions
SWMPr was developed to augment existing data management
programs (i.e., CDMO) by providing a bridge betwen the raw data and the
analysis software through its numerous data retrieval functions
(Table 1). Established QAQC methods and data
processing techniques are also enhanced with SWMPr by functions
that filter observations for different QAQC flags (qaqc)
and subset by selected dates or variables (subset).
Additionally, challenges comparing differents datasets are addressed by
the setstep and comb functions that
standardize and combine time series. Finally, the analysis functions
provide numerous tools to implement common analyses for time series and
more specific methods for water quality data. Further development of the
package will include modifications and additional functions to better
integrate data analysis with the quality of information provided by
SWMP. Several functions include default methods to extend use beyond the
"swmpr" object and additional development will continue to
focus on modifying the package to handle arbitrary data structures.
These challenges are not unique to the SWMP database such that many of
the functions will facilitate evaluations of more generic time series
datasets.
Acknowledgments
I acknowledge the significant work of NERRS researchers and staff that has allowed access to high-quality monitoring data. Thanks to Todd O’Brien for the inspiration for the online widgets. Thanks to Mike Murrell and Jim Hagy III for assistance with the ecosystem metabolism functions. Thanks to Jeff Hollister for providing useful comments on an earlier draft.