US Long-Term Ecological Research Network

Chloride Concentrations, Conductivity, and Water Temperature Data from Upper Yahara River Watershed Tributaries in Dane County, WI: December 2019 – April 2021

Abstract
Conductivity and chloride were measured for 2 years in nine tributaries of Lake
Mendota and Lake Monona in Dane County, WI. HOBO Conductivity loggers continuously
measured absolute conductivity and water temperature every 30 minutes. Breaks in
data collection were due to a calibration period or if the loggers were out of the
water. Grab samples for chloride concentration occurred weekly or biweekly.
Conductivity and water temperature were measured with a field meter at each sampling
excursion. This data was needed for a master’s research thesis with the goal of
characterizing the spatial distribution and loading of chloride in the Upper Yahara
River Watershed.<br/>
Core Areas
Dataset ID
406
Date Range
-
Methods
Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>Field measurements and lab analyses<br/>
Version Number
1

Chloride Concentrations, Conductivity, and Water Temperature Data from Lake Mendota and Lake Monona Madison, WI: December 2019 – April 2021

Abstract
Conductivity and chloride were measured for 2 years in Lake Mendota and Lake Monona in Madison, WI. Conductivity was continuously measured (every 30 minutes) on under-ice buoys in the eplimnia (1-2m below the surface) and hypolimnia (1m off the bottom of the lake) of the lakes. Depth-discrete chloride grab samples were collected from the lakes quarterly. Profile sampling in Mendota, which is approximately 25 m deep, occurred every 5m from 0-20m and at 23.5m. Profile sampling in Monona, which is approximately 21m deep, occurred every 4m from 0-20m. This data was needed for a master’s research thesis with the goal of identifying the lakes' mixing dynamics and how salinization may impact them.<br/>
Core Areas
Dataset ID
403
Data Sources
Date Range
-
Methods
Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>Field measurements and lab analysis<br/>
Version Number
1

North Temperate Lakes LTER: Physical and Chemical Limnology of Lake Kegonsa and Lake Waubesa 1994 - current

Abstract
Physical and chemicals parameters of two Madison-area lakes in the Yahara chain not included as core NTL-LTER study lakes. Parameters include intermittently sampled water temperature, dissolved oxygen, ph, total alkalinity, chloride and sulfate. Nutrient data has been collected since 2015. Number of sites: 2.
Dataset ID
401
Date Range
-
DOI
10.6073/pasta/cc6f0e4d317d29200234c7243471472a
Maintenance
ongoing
Metadata Provider
Short Name
NTLCH01
Version Number
1

Lake Mendota Multiparameter Sonde Profiles: 2017 - current

Abstract
Intermittent sensor profiling at the deep hole of Lake Mendota began in 2017 with a YSI EXO2 multiparameter sonde. Parameters include water temperature, pH, specific conductivity, dissolved oxygen, chlorophyll, phycocyanin, turbidity, and fDOM. Profiles are nominally 0 - 20 meters in depth in one meter increments, although the depth range and increments vary.

Core Areas
Dataset ID
400
Date Range
-
Instrumentation
YSI EXO2 Sonde
Publication Date
Version Number
1

Spatial heterogeneity of within-stream methane concentrations North Temperate Lakes LTER, 2014

Abstract
Streams, rivers, and other freshwater features may be significant
sources of CH4 to the atmosphere. However, high spatial and temporal
variabilities hinder our ability to understand the underlying
processes of CH4 production and delivery to streams and also challenge
the use of scaling approaches across large areas. We studied a stream
having high geomorphic variability to assess the underlying scale of
CH4 spatial variability and to examine whether the physical structure
of a stream can explain the variation in surface CH4. A combination of
high-resolution CH4 mapping, a survey of groundwater CH4
concentrations, quantitative analysis of methanogen DNA, and sediment
CH4 production potentials illustrates the spatial and geomorphic
controls on CH4 emissions to the atmosphere.
<br/>
Core Areas
Dataset ID
386
Date Range
-
LTER Keywords
Methods
We determined stream CH4 concentrations at a large spatial extent (10
km) and fine grain (35,000 total measurements) by using a
biogeochemical mapping platform on a small boat [Crawford et al.,
2015]. Water was pumped on board where gases were stripped from the
water by using a sprayer-type equilibrator and analyzed with a Los
Gatos Research ultraportable greenhouse gas analyzer (using a
cavity-enhanced absorption technique). Survey speeds were very slow
(less than 3 kph) to enable the detection of small-scale changes in
CH4 concentrations over short distances. CH4 concentrations were
corrected for hydraulic and equilibrator lags by using first-order
step-change experiments detailed in Crawford et al. [2015] following
the outline provided in Fozdar et al. [1985]. Lag-corrected CH4 values
were georeferenced by using concurrent Global Positioning System
readings with the Wide Area Augmentation System capability enabled.
The highresolution transect was sampled on 24 and 25 July 2014 (two
morning to afternoon segments were combined into one data set). Both
days were similar in terms of weather and in-stream conditions.
Maximum daily air temperatures were 22.4degC and 20.9degC. Mean daily
air temperatures were 18.5deg and 19.1degC. At the middle site, daily
mean discharges were 3.3 and 3.7 × 10 2 m3 (Julys mean Q is 3.7 × 10 2
m3). Mean water temperatures were 18.1degC and 17.8degC (Julys mean
temperature is 19.6).
Using the high-resolution spatial CH4 data sets, we assessed the
degree of spatial autocorrelation by using semivariograms (spherical
model, using the function autofitVariogram in the R package automap).
We focused on the semivariogram range parameter which describes the
average scale of autocorrelation (i.e., the average patch size). We
also assessed the structure of spatial autocorrelation by using the
global Morans I statistic. The Morans I statistic evaluates whether a
series of geospatial observations are randomly distributed in space
(the null model), clustered, or dispersed. Statistically significant
positive values indicate spatial clustering, whereas negative values
indicate dispersed patterns. We used the Anselin Local Morans I
statistic for spatial cluster analysis of high-resolution CH4 data
[Anselin, 1995]. Statistically significant values of Local Morans I
identify regions (clusters) of high or low values relative to the
global data set, in addition to outliers (e.g., low outliers
surrounded by high values). The analysis was executed by using the
Spatial Statistics toolbox in ArcMap 10.2.
Groundwater Methane Sources
We analyzed groundwater CH4 from a series of wells near the middle and
lower sites and from wells at the head of the drainage near the spring
ponds (upper site) during the time frame of this study by using a
headspace equilibration method [Striegl et al., 2001]. Wells were
developed by using a peristaltic pump, and a minimum of two well
volumes were purged before sample collection. The goal was to evaluate
additional (external) lateral and vertical sources of CH4 beyond the
hyporheic zone. Despite a relatively homogenous sand aquifer,
groundwater flow paths and residence times are complex in this
catchment [Pint et al., 2003; Walker et al., 2003]. A combination of
flow paths including deep groundwater derived from meteoric recharge,
deep groundwater derived from lakes, and meteoric riparian water all
contribute to surface flow in the catchment. These water sources and
flow paths have been studied for over a decade as part of the USGS
WEBB program [Pint et al., 2003; Walker et al., 2003]. Differences in
substrate, organic matter availability, oxygen conditions, and other
metrics of redox state were previously shown to relate to the
concentrations of dissolved gases in wells at the middle site based on
historical data [Crawford et al., 2014b]. Here we expand the survey to
correspond to the timing of surface water mapping and to determine
whether patterns previously observed at the middle site held for the
catchment in general.
Sediment Methanogen Distribution and Abundance
CH4 production potential within stream sediments was first determined
by extracting DNA from stream sediment cores and quantifying the
abundance of methanogenic Archaea. We collected 14 cores approximately
22 cm long in sand and organic-rich wetland locations near the middle
site (locations correspond to odd numbered transect locations in
Figure 1; also corresponding to CH4 bubble trap locations described in
Crawford et al. [2014b]). Sediment cores were collected by using a 2.5
cm diameter, 30 cm length, stainless steel corer with an internal
polycarbonate tube attached to a one-way flow valve and a PVC
extension. Intact cores were transported to the laboratory within 2 h
and immediately frozen. Sediment cores were split into 2 cm segments
followed by DNA extraction by using a PowerSoil DNA isolation kit
(MoBio Laboratories Inc., Carlsbad, CA). We used quantitative
polymerase chain reaction (qPCR) targeting the gene encoding the alpha
subunit of methyl coenzyme-M reductase (mcrA) to quantify both
longitudinal and vertical distributions of methanogens. The mcrA gene
encodes a component of the terminal enzyme complex in the methane
generation pathway and is thought to be unique to methanogens and well
conserved [Thauer, 1998]. Many previous studies have used mcrA as a
genetic marker to determine methanogen abundance and community
composition [Luton et al., 2002; Earl et al., 2003; Freitag et al.,
2010; West et al., 2012]. Each extracted sample containing mcrA was
amplified in a 20 uL qPCR reaction in an ep gradient s realplex2
master cycler (Eppendorf), using SYBR Green as the reporter dye. Each
reaction contained 1 uL of 1/10 diluted sample DNA template, 1× iQ
SYBR Green Supermix (Biorad), and 0.25 uM of each primer targeting
mcrA: mcrAqF (50-AYGGTATGGARCAGTACGA-30) and mcrAqR
(50-TGVAGRTCGTABCCGWAGAA-30) [West et al., 2012]. Thermocycling
conditions for the mcrA qPCR were as follows: an initial denaturation
at 94degC for 1 min, followed by 40 cycles of 94degC denaturation for
40 s, 54degC annealing for 30 s, 72degC elongation for 30 s, and a
fluorescent detection at 85degC for 20 s. Melting curves were run to
ensure absence of nonspecific amplification. Amplification,
fluorescence data collection, and initial data analysis were all
performed by using the Eppendorf realplex2 software (Eppendorf,
Hauppauge, NY, USA).
Despite collecting greater than 20 cores, we were not able to perform
cluster analysis similar to that for CH4 concentrations on the genetic
data because the sample size was too low. Instead, we elected to
compare organicrich versus sand sediment mcrA gene abundance by using
a t test. To determine if methanogen abundance was correlated with CH4
production, we fit a linear model (log transformed) of mcrA abundance
versus average CH4 ebullition documented in the same year [see
Crawford et al., 2014b] with the R statistical programming language [R
Core Team, 2014]. We contend that the comparison between microbial
communities and integrated CH4 bubble flux over time is a stronger
comparison than that of point measurements of CH4 concentration.
Sediment Methane Production Potential
We collected surface sediments from Allequash Creek and placed them in
sealed jars the morning of the start of laboratory experiments. These
sediments were presumed to be mostly anoxic per the oxygen profile
study by Crawford et al. [2014b]. In the lab, about 75 mL of water
saturated surface sediments was transferred into a 150 mL glass
container, flushed with N2, sealed with a gas-tight lid equipped with
a butyl rubber septum for headspace gas sampling, and placed on a
shaker table in the dark at room temperature (about 22degC). Gas
samples were collected after 24 h for CH4 determination by using a
Shimadzu GC-2014 gas chromatograph. Headspace volume of each sample
was determined after gas sampling, then sediments were transferred to
a preweighted aluminum pan for drying (72 h at 50degC) and ashing (4 h
at 500degC). CH4 production potential was determined as headspace CH4
accumulation per gram of dry sediment and per gram of ash-free dry
mass (AFDM) per hour. Production rates were based on two-point
measurements of CH4 concentration and are thus presumed to be linear
over time. Because gas production rates could not be transformed to
meet assumptions of normality, significant differences among
treatments were assessed by using a Kruskal-Wallis test followed by
Wilcoxon rank tests for pairwise comparisons with Bonferroni-adjusted
P value using R.
Version Number
1

WSC - The Influence of Legacy P on Lake Water Quality

Abstract
Using a suite of numerical models, we investigated the influence of legacy P on water quality in the Yahara Watershed of southern Wisconsin, USA. The suite included Agro-IBIS, a terrestrial ecosystem model; THMB, a hydrologic and nutrient routing model; and the Yahara Water Quality Model which estimates water quality indicators in the Yahara chain of lakes. Using five alternative scenarios of antecedent P storage (legacy P) in soils and channels under historical climate conditions, we simulated outcomes of P yield from the landscape, lake P loading, and three lake water quality indicators. Data and code may also be found in https://github.com/SRCarpen/Yahara2070LakeModel_Fits2data.git
Core Areas
Dataset ID
384
Date Range
-
Methods
We developed a watershed modeling framework that can simulate an array of ecosystem services, including land-to-lake flows of water, sediment, and nutrients, and surface water quality (Figure 1). The framework includes process-based representation of natural and managed ecosystems (Agro- IBIS), hydrologic routing of water, sediment, and nutrients through the surface hydrologic network (THMB), and prediction of Secchi disk depth (a measure of lake transparency), summertime lake total phosphorus (TP) concentration, and the probability of hypereutrophy in each lake (Yahara Water Quality Model).
for detailed description of modeling approach please see:
Motew, M., Chen, X., Booth, E.G. et al. The Influence of Legacy P on Lake Water Quality in a Midwestern Agricultural Watershed. Ecosystems 20, 1468–1482 (2017). https://doi.org/10.1007/s10021-017-0125-0
Version Number
1

Spatially Distributed Lake Mendota EXO Multi-Parameter Sonde Measurements Summer 2019

Abstract
This data was collected over 9 sampling trips from June to August 2019. 35 grid boxes were generated over Lake Mendota. Before each sampling effort, sample point locations were randomized within each grid box. Surface measurements were taken with an EXO multi-parameter sonde at the 35 locations throughout Lake Mendota during each sampling trip. Measurements include temperature, conductivity, chlorophyll, phycocyanin, turbidity, dissolved organic material, ODO, pH, and pressure.
Core Areas
Dataset ID
388
Date Range
-
Maintenance
ongoing
Methods
Conducted weekly data sampling (9 boat trips in June-August 2019) using an EXO multi-parameter sonde to collect temperature, conductivity, chlorophyll (ug/L), phycocyanin (ug/L), turbidity, dissolved organic material, ODO, pH, and pressure at 35 locations based on GPS guided stratified random sampling. 35 grid boxes were generated over Lake Mendota using qGIS. Point locations within each grid box were randomized before each sampling effort. At each point, variables were recorded continuously with the EXO sonde for a two-minute period. Continuous data was overaged over the two-minute period for each sample point.
Publication Date
Version Number
1

North Temperate Lakes LTER Regional Survey water temperature DO 2015 - current

Abstract
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry.
Water temperature and dissolved oxygen profiles were taken on sampling days.
Contact
Dataset ID
382
Date Range
-
Maintenance
ongoing
Methods
water temperature and dissolved oxygen were measured at 1 meter intervals with a opto sonde
Version Number
1

North Temperate Lakes LTER Regional Survey Water Chemistry 2015 - current

Abstract
The Northern Highlands Lake District (NHLD) is one of the few regions in the world with periodic comprehensive water chemistry data from hundreds of lakes spanning almost a century. Birge and Juday directed the first comprehensive assessment of water chemistry in the NHLD, sampling more than 600 lakes in the 1920s and 30s. These surveys have been repeated by various agencies and we now have data from the 1920s (UW), 1960s (WDNR), 1970s (EPA), 1980s (EPA), 1990s (EPA), and 2000s (NTL). The 28 lakes sampled as part of the Regional Lake Survey have been sampled by at least four of these regional surveys including the 1920s Birge and Juday sampling efforts. These 28 lakes were selected to represent a gradient of landscape position and shoreline development, both of which are important factors influencing social and ecological dynamics of lakes in the NHLD. This long-term regional dataset will lead to a greater understanding of whether and how large-scale drivers such as climate change and variability, lakeshore residential development, introductions of invasive species, or forest management have altered regional water chemistry. The regional lakes survey in 2015 followed the standard LTER protocol for standard water chemistry and biology. Samples were taken as close to solar noon as possible. Seven lakes had replicates performed, which were chosen at random.
Contact
Dataset ID
380
Date Range
-
Maintenance
ongoing
Methods
Inorganic and organic carbon
Inorganic carbon is analyzed by phosphoric acid addition on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer.
Organic carbon is analyzed by combustion, on a Shimadzu TOC-V-csh Total Organic Carbon Analyzer.
Version Number
2

Molecular composition of dissolved organic matter in NTL-LTER lakes detected by Fourier-transform ion cyclotron resonance mass spectrometry

Abstract
The composition of dissolved organic matter (DOM) varies widely in the environment due to distinct sources of the material and subsequent processing. DOM composition drives its reactivity in terms of many processes including photochemical reactions, microbial metabolism, and carbon cycling within water bodies. This study uses ultra-high resolution mass spectrometry via a Fourier-transform ion cyclotron resonance mass spectrometer (FT-ICR MS) to evaluate DOM composition at the molecular level to determine differences in DOM composition among the NTL-LTER lakes. Whole water samples were collected from the surface of each lake near the shore on August 18th and 19th in 2016 in. Ultraviolet-visible spectra were recorded as light absorbance can also give information about DOM composition. Additionally, concentrations of anions, cations, and pH were measured waters because these can all alter DOM reactivity in the environment. Both water chemistry and DOM composition vary widely among the lakes with the bogs displaying the most terrestrial-like signature in DOM and the oligotrophic lakes show more microbial-like or environmentally processed DOM.
Core Areas
Dataset ID
378
Date Range
-
Maintenance
comleted
Methods
Molecular Composition

Water was acidified to pH = 2 with concentrated hydrochloric acid and organic matter was extracted from the water using Agilent PPL cartridges. Extracts were diluted 100x in 50:50 acetonitrile to ultra-pure water and directly injected into a Bruker SolarX 12T Fourier-transform ion cyclotron resonance mass spectrometer. Ionization was achieved with electrospray ionization by an Advian NanoMate delivery system in both positive and negative mode.

Version Number
2
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