Methods

Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>Nutrients were added to Peter (2013-2015, 2019) and Tuesday (2013-2015)
lakes to cause algal blooms. Details on nutrient additions (start/end dates,
loading rates, N:P ratios) are described in Buelo et al. 2022 (Ecological
Applications, link below), Wilkinson et al. 2018 (Ecological Monographs 88:
188-203), and Pace et al. 2017 (Proceedings of the National Academy of
Sciences USA 114: 352-357). These publications including supplements should
be consulted for details. These lakes have been used for whole-ecosystem
experiments over the past decades; see Carpenter and Pace 2018 (Limnology
and Oceanography Letters 3(6): 419-427) for an overview.<br/>

Methods

Nutrients were added to Peter and Tuesday lakes to cause algal blooms. Details on nutrient additions (start/end dates, loading rates, N:P ratios) are described in Wilkinson et al. 2018. (Ecological Monographs 88:188-203). Methods are described in Pace et al. 2021 (Limnology and Oceanography linked below), Wilkinson et al. 2018 (Ecological Monographs 88:188-203), and Pace et al. 2017 (Proceedings of the National Academy of Sciences USA 114: 352-357). These publications including supplements should be consulted for details.<br/>Nutrients were added to Peter and Tuesday lakes to cause algal blooms. Details on nutrient additions (start/end dates, loading rates, N:P ratios) are described in Wilkinson et al. 2018. (Ecological Monographs 88:188-203). Methods are described in Pace et al. 2021 (Limnology and Oceanography linked below), Wilkinson et al. 2018 (Ecological Monographs 88:188-203), and Pace et al. 2017 (Proceedings of the National Academy of Sciences USA 114: 352-357). These publications including supplements should be consulted for details.<br/>Nutrients were added to Peter and Tuesday lakes to cause algal blooms. Details on nutrient additions (start/end dates, loading rates, N:P ratios) are described in Wilkinson et al. 2018. (Ecological Monographs 88:188-203). Methods are described in Pace et al. 2021 (Limnology and Oceanography linked below), Wilkinson et al. 2018 (Ecological Monographs 88:188-203), and Pace et al. 2017 (Proceedings of the National Academy of Sciences USA 114: 352-357). These publications including supplements should be consulted for details.<br/>Nutrients were added to Peter and Tuesday lakes to cause algal blooms. Details on nutrient additions (start/end dates, loading rates, N:P ratios) are described in Wilkinson et al. 2018. (Ecological Monographs 88:188-203). Methods are described in Pace et al. 2021 (Limnology and Oceanography linked below), Wilkinson et al. 2018 (Ecological Monographs 88:188-203), and Pace et al. 2017 (Proceedings of the National Academy of Sciences USA 114: 352-357). These publications including supplements should be consulted for details.<br/>

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.

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.

Methods

Methods are described in Wilkinson et al. 2018 (Ecological Monographs 88:188-203) and Pace et al. 2017 (Proceedings of the National Academy of Sciences USA 114: 352-357). These publications including supplements should be consulted for details.

Methods

Methods are described in Pace and Cole 2002 (https://doi.org/10.4319/lo.2002.47.2.0333). Surface water samples for the analysis of pH, dissolved organic carbon (DOC), chlorophyll a , total phosphorus color were collected by dipping a sample bottle. The total phosphorus (TP) samples were stored in a separate acid-washed bottle. Samples were collected monthly from May through August from a set of 20 lakes for the years 1995-2000. A subset of fifteen lakes were sampled in the same way from 2001-2003. Samples were stored in a cooler and returned the lab for processing within a few hours.

Methods

Data were collected at 5 minute intervals using in-situ automated sensors (sondes). All measurements and samples were collected from a stationary raft over the deepest part of the lake.
Sondes were suspended from floats with probes at a depth of 0.75m below the surface. Sonde sensors were cleaned daily in the field and calibrated monthly following manufacturer guidelines. Peter and Paul lakes were each monitored with two YSI multiparameter sondes (model 6600 V2-4) fitted with optical DO (model 6150), pH (model 6561), optical Chl-a (model 6025), and conductivity-temperature (model 6560) probes. Sensor measurements were made at 0.75 m every 5 min and were calibrated weekly. PAR was measured and the UNDERC meteorology station maintained by the University of Notre Dame or by the North Temperate Lakes Weather Station at Woodruff Airport.
Outliers were replaced by NA. Occasional gaps in the record due to instrument cleaning are NA.

Methods

General: Bade, D., J. Houser, and S. Scanga (editors). 1998. Methods of the Cascading Trophic Interactions Project. 5th edition. Center for Limnology, University of Wisconsin-Madison, and Cary Institute of Ecosystem Studies, Millbrook, NY.

Methods

In two consecutive years, we measured lake-wide spatial patterning of cyanobacteria using the FLAMe platform (Crawford et al. 2015). To evaluate early warning indicators of a critical transition, in the first year we induced a cyanobacteria bloom through nutrient addition in an experimental lake while using a nearby unmanipulated lake as a reference ecosystem (Pace et al. 2017). During the second year, both lakes were left unmanipulated. Proposed detection methods for early warning indicators were compared between the manipulated and reference lakes to test for their ability to accurately detect statistical signals before the cyanobacteria bloom developed.
Peter and Paul Lakes are small, oligotrophic lakes (Peter: 2.5 ha, 6 m, 19.6 m and Paul: 1.7 ha, 3.9 m, 15 m, for surface area, mean, and max depth respectively) located in the Northern Highlands Lake District in the Upper Peninsula of Michigan, USA (89°32’ W, 46°13’ N). These lakes have similar physical and chemical properties and are connected via a culvert with Paul Lake being upstream. Both lakes stratify soon after ice-off and remain stratified usually into November (for extensive lake descriptions, see Carpenter and Kitchell, 1993).
In the first year, Peter Lake was fertilized daily starting on 1 June 2015 (DOY 152) with a nutrient addition of 20 mg N m-2 d-1 and 3 mg P m-2 d-1 (molar N:P of 15:1) through the addition of H3PO4 and NH4NO3 until 29 June (day of year, DOY 180). The decision to stop nutrient additions required meeting four predefined criteria based on temporal changes in phycocyanin and chlorophyll concentrations indicative of early warning behavior of a critical transition to a persistent cyanobacteria bloom state. (Pace et al. 2017). Nutrients uniformly mix within 1-2 days after fertilization based on prior studies (Cole and Pace 1998). No nutrient additions were made to Paul Lake. In the second year (2016), neither lake received nutrient additions.
We mapped the surface water characteristics of both experimental lakes to identify changes in the spatial dynamics of cyanobacteria. In 2015, mapping occurred weekly from 4 June to 15 August (11 sample weeks). In 2016, when neither lake was fertilized, the lakes were mapped three times in early to mid-summer. In both years, mapping occurred between the hours of 07:00 to 12:00 (before the daily nutrient addition). We rotated the order that we sampled the lakes to avoid potential biases due to differences in time of day. Each individual lake sampling event was completed in approximately one hour.
The FLAMe platform maps the spatial pattern of water characteristics. A boat-mounted sampling system continuously pumps surface water from the lake to a series of sensors while geo-referencing each measurement (complete description of the FLAMe platform in Crawford et al. 2015). For this study, the FLAMe was mounted on a small flat-bottomed boat propelled by an electric motor and was outfitted with a YSI EXO2™ multi-parameter sonde (YSI, Yellow Springs, OH, USA). We focused for this study on measures of phycocyanin (a pigment unique to cyanobacteria) and temperature. Phycocyanin florescence was measured using the optical EXO™ Total Algae PC Smart Sensor. The Total Algae PC Smart Sensor was calibrated with a rhodamine solution based on the manufacturer’s recommendations. Phycocyanin concentrations are reported as ug/L; however, these concentrations should be considered as relative because we did not calibrate the sensor to actual phycocyanin nor blue-green algae concentrations. Geographic positions were measured using a Garmin echoMAP™ 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Each sampling produced approximately 3500 measurements in the manipulated lake and 2000 in the reference lake. The measurements were distributed by following a gridded pattern across the entire lake surface to characterize spatial patterns over the extent of the lake.

Methods

The Fast Limnology Automated Measurement (FLAMe) platform is a novel flow-through system designed to sample inland waters at both low- (0 to appr. 10 km hr-1) and high-speeds (10 to greater than 45 km hr-1) described in Crawford et al. (2015). The FLAMe consists of three components: an intake manifold that attaches to the stern of a boat; a sensor and control box that contains hoses, valves, a circulation pump and sensor cradles; and a battery bank to power the electrical components. The boat-mounted intake manifold serves multiple purposes. First, sensors are mounted inside the boat, protecting them from potential damage. Second, the intake system creates a constant, bubble-free water flow, thus preventing any issues for optical sensors due to cavitation. Finally, to analyze dissolved gases, a constant water source is needed on board. Water flow via both the slow- and high-speed intakes is regulated by the onboard impeller pump, allowing for seamless switching between slow- and high-speed operations. Any number of sensors could be integrated into the platform with simple modifications, and can be combined with common limnological instruments such as acoustic depth-finders. In our example applications we used a YSI EXO2 multiparameter sonde (EXO2; Yellow Springs, OH, USA), and a Satlantic SUNA V2 optical nitrate (NO3) sensor (Halifax, NS, Canada), both integrated into the control box plumbing with flow-through cells available from the manufacturer. Additionally, a Los Gatos Research ultraportable greenhouse gas analyzer (UGGA) (cavity enhanced absorption spectrometer; Mountain View, CA, USA) was used to measure dry mole fraction of carbon dioxide (CO2) and methane (CH4) dissolved in surface water by equilibrating water with a small headspace using a sprayer-type equilibration system that has previously been shown to have fast response times relative to other designs16 (Figure S1). Both the EXO2 and the UGGA are capable of logging data at 1 Hz. Because the SUNA was operated out of the water and on a boat during warm periods, data were collected less frequently (appr. 0.1 Hz) to minimize lamp-on time and avoid the lamp temperature cutoff of 35° C. The EXO2 sonde uses a combination of electrical and optical sensors for: specific conductivity, water temperature, pH, dissolved oxygen, turbidity, fluorescent dissolved organic matter (fDOM), chlorophyll-a fluorescenece, and phycocyanin fluorescence. The SUNA instrument measures NO3 using in situ ultraviolet spectroscopy between 190-370 nm, has a detection range of 0.3-3000 microM NO3, and a precision of 2 microM NO3. The UGGA has a reported precision of 1 ppb (by volume). In order to translate time-series data from the instruments into spatial data, we also logged latitude and longitude at 1 Hz with a global positioning system (GPS) with the Wide Area Augmentation System (WAAS) functionality enabled allowing for less than 3 m accuracy for 95percent of measured coordinates. Synchronized time-stamps from the EXO2, UGGA, SUNA, and GPS were used to combine data streams into a single spatially-referenced dataset.
We ran a simple set of experiments to determine the residence time of the system and the overall response time of the EXO2 and UGGA sensors integrated into the platform. After determining first-order response characteristics of each sensor, we applied an ordinary differential equation method to correct the raw data for significant changes in water input resulting in higher accuracy spatial data (see Crawford et al. 2015).
Sensor response experiments
We conducted a series of sensor response experiments on Lake Mendota on August 1, 2014. The goal was to understand the potential lags and minimum response times for the EXO2 and UGGA sensors integrated into the FLAMe platform. These data were then used to develop correction procedures for higher accuracy spatial datasets. To test sensor responses to step-changes in water chemistry, we mixed a 40 L tracer solution into a plastic carboy that was connected to the reservoir port on the FLAMe. The reservoir was mixed with 50 mL of rhodamine WT to test the phytoplankton fluorescence sensors, 6 mL of quinine sulfate solution in acid buffer (100 QSE) to test the fDOM sensor, 14 g of KCl to test the conductivity sensor, and appr. 2 kg of ice to reduce the temperature of the solution relative to lake water. The mixture volume was increased to 40 L using tap water. We did not modify the CO2 concentration or pH in the carboy as we found the municipal water source to have greater than ambient lake CO2 (4300 vs. 290 microatm, respectively) and lower pH (7.5 vs 8.3, respectively). At the beginning of the experiments, we allowed lake water to circulate through the system for appr. 10 minutes. We then switched to the tracer solution for a period of five minutes, followed by five minutes of lake water, then back to the tracer solution for an additional five minutes.
Using the step-change experiment data, we determined each sensors hydraulic time constant (Hr) and parameter time constant (taus). The sensor-specific Hr is a function of system water residence time and sensor position/shielding within the system. Taus is the time required for a 63 percent response to a step-change input. Hr was calculated based on the plateau experiments and was indicated by the first observation with a non-zero rate of change. The CO2 and CH4 sensors had a much greater Hr than the EXO2 sensors because water must travel further through the system before equilibrating with the gas solution being pumped to the UGGA. Using these Hr values, we offset response variables thus removing the hydraulic lag. This correction does not account for sensor-specific response patterns (tau s). The EXO2 sensors have manufacturer-reported taus values between 2-5 s, but these values are not appropriate to apply to the FLAMe system because they do not include system hydraulic lag and mixing. In order to match sensor readings with spatial information, we first applied Hr values from each sensor output according to equation 2. This step aligns the time at which each sensor begins responding to the changing water, and accounts for the physical distance the water must travel before being sensed
In order to match individual sensor response characteristics and to obtain more accurate spatial data, we then applied sensor-specific corrections using Equation 3 (Fofonoff et al., 1974).
We first smoothed the raw data using a running mean of 3 observations in order to reduce inherent noise of the 1 Hz data. We then calculated dX/dt using a 3-point moving window around Xc. Equation 3 should ideally lead to a step response to a step-change input. We note that this is the same strategy used to correct oceanographic conductivity and temperature instruments (see Fozdar et al., 1985). Overall, the taus-corrected data show good responses to step-change inputs and indicate that this is a useful technique for generating higher accuracy spatial data. We include three types of data for each variable including: raw (e.g., TempC), the hydraulic lag corrected (e.g., TempC_hydro) and the taus-corrected data (e.g., TempC_tau). Note that not all sensors were used in each survey and not all sensors have each type of correction. This data was from our preliminary FLAMe sampling campaigns and future studies will include additional sensor outputs and corrections.
We used the FLAMe throughout the summer of 2014 on four distinct aquatic ecosystems including: a small dystrophic lake, a stream/lake complex, a medium-sized eutrophic lake, and a managed reach of the Upper Mississippi River. Each of these applications demonstrates the spatial variability of surface water chemistry and the flexibility of FLAMe for limnological research.
References
Crawford JT, Loken LC, Casson NJ, Smith C, Stone AG, and Winslow LA (2015) High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science and Technology 49:442-450.
Fozdar FM, Parker GJ, and Imberger J (1985) Matching temperature and conductivity sensor response characteristics. Journal of Physical Oceanography 15:1557-1569.