US Long-Term Ecological Research Network

Lake snow removal experiment phytoplankton community data, under ice, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time
of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging
research points to a future of reduced ice cover duration and changing snow conditions that
will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice
conditions, and subsequent changes to under-ice light environment, might impact ecosystem
dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from
a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during
the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from
the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019
season served as a reference year, and snow was not removed from the lake. This dataset
represents phytoplankton community samples (pooled epilimnion and hypolimnion samples
representative of 7 m water column) both under-ice and during some shoulder-season (open
water) dates. Samples were collected into amber bottles and preserved with Lugol's solution
before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic
identification and quantification.<br/>
Core Areas
Creator
Dataset ID
418
Data Sources
Date Range
-
Methods
Phytoplankton samples were obtained from the epilimnion and hypolimnion by
slowly lowering weighted Tygon tubing through the water column to a depth of 7 m, such
that the tubing was filled with a representative water column sample. Based on the inner
diameter of the tubing, 205 mL of water was pumped from the tubing for the epilimnion
sample. Next, 267 mL of water was pumped from the tubing for the hypolimnion sample. Each
sample was collected into a 250 mL amber bottle that contained 2 mL of Lugol’s solution.
Phytoplankton samples were pooled by sampling date and sent to Phycotech Inc. (St. Joseph
MI, USA) for phytoplankton identification, and concentration and biovolume
quantification.<br/>
Publication Date
Version Number
1

Aquatic snail and macrophyte abundance and richness data for ten lakes in Vilas County, WI, USA, 1987-2020

Abstract
Data accompanying the paper Szydlowski et al. "Three decades of lake monitoring
reveals community recovery after population declines of invasive rusty crayfish (Faxonius
rusticus)." Macrophytes and snails were sampled in ten lakes in Vilas County, Wisconsin, USA
during summer sampling events in 1987, 2002, 2011, and 2020. Lakes had varying levels of
invasion by F. rusticus, which affected measures of macrophytes and snails. Macrophytes were
sampled using a point-intercept transect method and snails were sampled using different
sampler types which were dependent on substrate. Macrophytes were sampled at 6-14 sites per
lake and snails were sampled at 16-31 sites per lake. Overall, this dataset provides abundance
and richness data for over 25 species of snails and over 40 species of macrophytes in north
temperate lakes.<br/>
Core Areas
Dataset ID
417
Methods
Sampling methods are described by Szydlowski et al. "Three decades of lake
monitoring reveals community recovery after population declines of invasive rusty crayfish
(Faxonius rusticus)," but are provided here for convenience. Instrumentation is further
documented in the supplementary information of the paper. Macrophytes were sampled during
July and August at a subset of crayfish sampling sites within our ten study lakes (n =
6–14 sites per lake) that were selected in 1987 to capture a variety of substrates and
both east and west sun exposure. Sampling depths were randomly assigned to sites during
initial sampling in 1987 as either 0.75 m, ½ of Secchi depth, or ¾ of Secchi depth, with
1987 Secchi depths used for all subsequent sampling years for macrophyte surveys. We
followed the line-intercept method to sample macrophytes, using snorkeling and SCUBA to
visually identify and determine the presence or absence of macrophyte species along a 25 m
transect set parallel to shore at the pre-determined depth for each sampling site.
Transects were marked at 1 m intervals, with the first 10 cm of each interval marked by a
band of tape. Divers moved along the transect recording the presence or absence of each
macrophyte species crossing the vertical plane of each 10 cm band. The line-intercept
method allowed us to obtain a measure of both macrophyte species richness and abundance.
Because just presence or absence of macrophyte species was recorded, and only at each 10
cm band, our measurements provide an index for abundance and a minimum estimate for
species richness. Freshwater snails were sampled at locations historically sampled for
crayfish (n = 24 or 36 sites per lake) between late June and early August. As with
macrophytes, snails were sampled at randomly assigned depths of either 0.75 m, ½ of Secchi
depth, or ¾ of Secchi depth. While the same absolute depths were used in 1987 and 2002
based on 1987 Secchi values, depths in 2011 and 2020 were determined using year-specific
Secchi values. Most sampling depths in 2011 and 2020 varied only slightly from the 1987
and 2002 values, but in two lakes the change in sampling depth was greater than one meter
due to larger shifts in water clarity. The greatest changes in sampling depth (2.7 m in
Papoose Lake and 1.5 m in Little John Lake) occurred at the ¾ Secchi depth sites, whereas
the ½ Secchi depth sites were less affected by the change in water clarity in these two
lakes. We sampled snails using methods and equipment designed for each habitat type
present in our study lakes (soft substrates, macrophytes, and cobble). For soft substrates
such as sand and muck (flocculent sediment or sediment rich in organic material), we used
a cylindrical polyvinyl chloride (PVC) sediment corer (0.018 m2), which we used to take a
5 cm sediment core. For sites with soft substrates where macrophytes were present, we used
a modified PVC sampler of the same size but with two hinged PVC halves, and a net made of
1-mm mesh attached to the top. We carefully closed the two halves of the PVC sampler
around macrophytes growing at the surface and zippered the mesh net around taller
macrophytes before pushing the corer into the sediment to collect a 5 cm core. Collecting
the macrophyte material along with the sediment allowed us to sample any snails on the
macrophytes along with those in the sediment. At the water’s surface we sieved (with 1 mm
mesh) all cores from soft substrates to remove fine sediments and large particles and
picked through macrophyte material for snails. Finally, for cobble habitats, we placed a
ring (0.1 or 0.5 m2) on the substrate at each site to define a sampling area. In 1987 and
2002, the 0.1 m2 ring was used for sites with a high density of snails, and the 0.5 m2
ring was used for sites with a low density of snails. In 2011 and 2020, we used the 0.5 m2
ring at all sites. We gently collected the surface layer of rocks within the sampling ring
and briefly brought the rocks to the surface, where we scraped attached material into a
collection pan and funneled it through a 1 mm mesh sieve to gather snails. We stored
snails collected using all sampling methods in 70% ethanol for later identification. In
the lab, we picked snails from all samples and identified them to species or genus (for
Physella sp.) according to Burch (1989) and Johnson et al. (2013), with revisions for
Lymnaeidae (Hubendick 1951) and Planorbidae (Hubendick and Rees 1955). We calculated snail
abundance as density to account for differences between the sediment corers and the rings
in area sampled. Snail samples from 1987 were lost in a laboratory flood, but specimens
from 2002 and 2011 are vouchered at the Notre Dame Museum of Biodiversity in Notre Dame,
Indiana, USA. Specimens from 2020 are vouchered at the Illinois Natural History Survey
Mollusk Collection at the University of Illinois in Champaign, Illinois, USA. In 2020, we
were not able to sample macrophytes and snails using SCUBA due to limitations from the
COVID-19 pandemic. Therefore, we excluded a small portion of deeper sites (approximately
2% of total macrophyte sites and 13% of total snail sites) that could not be sampled
accurately and safely while snorkeling. In addition, because of a few lost samples, data
from previous sampling years were not always available for each site. Consequently, in our
datasets of macrophytes and snails, we only include sites for which we had data in all
four sampling years (n = 100 sites/year for macrophytes, n = 208 sites/year for snails).
In our snail data, we only included snails which were alive at the time of sampling (i.e.,
we did not include empty shells).<br/>
Publication Date
Version Number
1

Lake snow removal experiment zooplankton community data, under ice, 2019-2021

Abstract
Although it is a historically understudied season, winter is now recognized as a time
of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging
research points to a future of reduced ice cover duration and changing snow conditions that
will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice
conditions, and subsequent changes to under-ice light environment, might impact ecosystem
dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from
a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during
the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from
the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019
season served as a reference year, and snow was not removed from the lake. This dataset
represents under ice zooplankton community samples (integrated tows at depths of 7 m) and some
shoulder-season (open water) zooplankton community samples. Zooplankton samples were preserved
in 90% ethanol and later processed to determine taxonomic classification at the species-level,
density (individuals / L), and average length (mm).<br/>
Contact
Core Areas
Creator
Dataset ID
414
Date Range
-
Methods
Our study lake, South Sparkling Bog (SSB) (46.003°N, 89.705°W), is a bog lake
located in Vilas County in Northern Wisconsin. South Sparkling Bog is a dystrophic,
dimictic lake with a maximum depth of 8 m, a mean depth of 3.6 m, and a surface area of
0.44 ha. South Sparkling Bog is surrounded by a sphagnum bog mat and has no shoreline
development. During the winters of 2019-2020 and 2020-2021, snow was removed from the
surface of South Sparkling Bog following any snow accumulation event. Removal was
conducted via a snowplow attached to the front of an ARGO all-terrain vehicle and a
snowblower. The winter of 2018-2019 served as a reference year, and snow was not removed
from South Sparkling Bog’s surface. While ice cover persisted, plankton samples were
collected at the deep spot for each lake during on a biweekly-to-monthly basis each
winter. On each sampling date, one integrated zooplankton tow was taken at a depth of 0-7
m using a 56 µm mesh Wisconsin net. All zooplankton samples were collected into glass
sample jars, preserved in 90% ethanol, and saved for laboratory analysis. In the lab,
zooplankton samples were filtered through 53 µm mesh and diluted to a known volume, and
three sub-sample replicates were taken using a 1 mL Hensen-Stempel pipette. Sub-sample
replicates were counted to at least 100 individuals, otherwise the entire sample was
quantified. Sub-sample data was then converted to the known diluted volume and finally
converted to total filtered volume (from the integrated tow sample) to estimate density
(individuals L-1). Zooplankton samples were processed using a Leica M8Z dissecting scope
and Leica imaging software. Replicate subsamples were averaged to estimate total abundance
and density, and average lengths (mm) for each sample taxa were calculated from measures
of the first 30 taxa found within a sample date.<br/>
Publication Date
Version Number
1

Cascade Project at North Temperate Lakes LTER – Daily Bloom Data for Whole Lake Experiments 2011 - 2019

Abstract
Daily measurements of algal bloom variables (chlorophyll, phycocyanin
fluorescence, dissolved oxygen, and pH) from the surface waters of Paul, Peter, and
Tuesday lakes from mid-May to early September for the years 2011 to 2019, excluding
2012 and 2017. In some years, Peter (2013-2015, 2019) and Tuesday (2013-2015) lakes
had inorganic nitrogen and phosphorus added to them daily to cause algal blooms
while Paul Lake served as an unmanipulated reference.<br/>
Core Areas
Dataset ID
413
Data Sources
Date Range
-
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/>
NTL Themes
Version Number
1

Fish catch and biomass per unit effort from McDermott and Sandy Beach Lakes 2017-2020

Abstract
Centarchidae spp., a warm-adapted group of fishes including basses and sunfishes, has increased in recent decades in Wisconsin. Concurrently, declines in cool-adapted species, including Walleye (Sander vitreus), have occurred but the cause is not understood. Multiple factors have been associated with these declines, including rising lake temperatures, habitat degradation, harvest, and species interactions. To quantify the role that competition and/or predation between increasing centrarchids and the rest of the fish community plays, we are conducting a whole-lake experiment to remove centrarchids from an experimental lake in northern Wisconsin while measuring the response of all other fish species. In 2018 and 2019, ~200,000 centrarchid individuals were removed, while species-specific catch-per-unit-effort (CPUE) and biomass-per-unit-effort (BPUE) were measured. Yellow Perch have increased in CPUE and BPUE, while centrarchid abundances have declined. We will continue removing centrarchids in 2021 and monitoring these populations. This information will be used to inform an understanding of the conditions necessary to support self-sustaining fish populations given global environmental change.<br/>
Core Areas
Creator
Dataset ID
398
Date Range
-
Maintenance
ongoing
Methods
Study area
The experimental (McDermott Lake; 46.00299280, -90.16081610) and reference (Sandy Beach Lake (46.10614350, -89.97131020) systems are located in Iron County in Northern, Wisconsin. McDermott Lake has a surface area of 33.1 ha and a mean depth of 3.0 m, while Sandy Beach Lake has a surface area of 44.5 ha and a mean depth of 2.1 m. Maximum depth in McDermott Lake is 5.7 m and in Sandy Beach Lake is 4.0 m. Both lakes include a variety of substrates (e.g., rock, gravel, and sand) and areas of submerged and emergent vegetation. At the start of the study, McDermott and Sandy Beach Lake fish communities were similar with high Centrarchidae spp. (i.e., centrarchid) abundances, few adult walleye, and a history of natural walleye recruitment. Other species present include yellow perch, northern pike, muskellunge, black bullhead, and white sucker (Table 1).
Fish sampling
Standardized surveys
From 2017-2020, we conducted fish population sampling on the experimental (McDermott) and reference (Sandy Beach) lakes. Every year of the study, we have conducted standardized monitoring surveys employing numerous sampling techniques to detect changes in the fish community relative to 2017-baseline information (Fig. 1). Sampling began immediately following ice-out (~mid-April) with the deployment of five fyke nets for one week. The fyky surveys served two purposes: 1) to serve as the capture method for marking walleye as part of the mark-recapture survey to attain a population estimate, and 2) to estimate other focal species (i.e., black crappie, yellow perch, muskellunge, northern pike) relative abundances. During these surveys, all collected walleye were measured (total length (TL); mm), sexed, checked for a Passive Integrated Responder (PIT) tag, and if one was not present, marked with a unique PIT tag. We also removed a dorsal spine sample for aging. Adult (mature) walleyes were defined as all fish 381 mm and all fish for which sex could be determined (regardless of length). Walleye of unknown sex &lt;381 mm were classified as juvenile (immature). McDermott and Sandy Beach Lakes have both had walleye population estimates previously conducted by the Wisconsin Department of Natural Resources (WDNR) therefore the goal was to mark 10% of the anticipated spawning population (based off of previous population estimates). Marking continued until the target number was reached or spent females began appearing in the fyke nets. Walleye were recaptured using an AC boat electrofishing survey within one week (typically 1-4 days) after netting and marking were completed. In each lake, the entire shoreline was sampled. All captured walleyes were measured and examined for marks. Based on electrofishing mark-recapture data, population estimates were calculated using the Chapman (1951) modification of the Petersen Estimator as:
N=((M+1)(C+1))/((R+1))
where N was the population estimate, M was the number of fish marked and released, C was the total number of fish captured and examined for marks in the recapture sample, and R was the total number of marked fish observed in C. The Chapman Modification method was used because it provides more accurate population estimates in cases when R is relatively small (Ricker 1975).

From early-May to mid-June, we sampled larval fishes using a 1,000m mesh conical ichthyoplankton net towed for five minutes immediately below the lake surface. Weekly samples were taken at night at five sampling locations in each lake. Each lake was divided into five quadrats and sites were established at a randomly selected nearshore (&lt;100 m of shore) location in each quadrat on each sampling date. Once selected, locations remained fixed throughout the study. Volume of water filtered during each tow was estimated using a General Oceanics© model 2030R flowmeter mounted in the center of the net frame. Samples were transferred to containers and stored in 90% ethanol. Collected fishes were identified to species according to Auer (1982) and enumerated.
In addition to the adult walleye population, we were interested in estimating the size of the adult largemouth bass population. We performed early summer (late-May) mark-recapture surveys using AC boat electrofishing (Wisconsin‐style; AC; 2.0–3.0 amps, 200–350 V, 25% duty cycle with two netters) to sample largemouth bass. Collected largemouth bass were measured, checked for a top caudal fin clip, and if not present, marked with a top caudal fin clip and released. Adult (mature) largemouth bass were defined as all fish 203 mm. We aimed to recapture 10% of the marked population. Largemouth bass are in very low abundance in Sandy Beach Lake therefore a population estimate was not possible. In McDermott Lake, due to the small population size of largemouth bass we completed multiple marking surveys from late-May to early June to achieve this recapture rate. From electrofishing mark-recapture information, population estimates were calculated using the Schnabel (1938) modification of the Lincoln-Petersen method:
where N was the population estimate, M was the number of fish marked and released in sample t, C was the number of fish captured in sample t, and R was the number of fish already marked when caught in sample t.
To obtain centrarchid population demographic data, current standardized WDNR surveys of inland lakes consist of early summer (water temperature range = 13.0–21.0°C) AC boat electrofishing surveys or mid‐summer (18.3–26.7°C) mini‐fyke net (Simonson et al. 2008). To encompass this range of water temperatures, we performed a combination of surveys starting at the end of May with an AC boat electrofishing survey. Then, fish were sampled once monthly when lake surface water temperatures were ≥13.0°C in both lakes (June–September). Both lakes were sampled during 1‐week each month using three gears (AC boat electrofishing, mini-fyke nets, cloverleaf traps). Lakes were sampled on consecutive nights in each 1‐week period but only one gear type was employed per night.
All gears sampled shallow shorelines (0–5 m from bank, depth ≤2 m) and were deployed in fixed locations following standard approaches (Bonar et al. 2009). Sampling locations were evenly distributed along the shoreline of the lake, and all gears were deployed in similar habitat types. Five 10‐min nighttime boat electrofishing (Wisconsin‐style; AC; 2.0–3.0 amps, 200–350 V, 25% duty cycle) transects were conducted using two dipnetters. Five mini‐fyke nets (0.9‐m × 0.61‐m frames, 3.2‐mm mesh [bar measure], 7.6‐m‐long lead, and a double throat) were deployed in areas where the net frames would be in 1.0–1.5 m of water, and leads were fixed onshore. Five cloverleaf traps (three lobed, height = 41 cm, 50 cm diameter, 6.0‐mm bar wire mesh with 12.7‐mm‐wide openings between lobes, and an attractant [liver]) were deployed in littoral habitats. Both mini‐fyke nets and cloverleaf traps were set in early afternoon, fished overnight, and retrieved the following afternoon (~24‐h soak time). All catches were standardized according to gear-specific effort. For boat electrofishing, catch per unit effort (CPUE) is presented as fish/hr. For mini-fyke nets and cloverleaf traps, CPUE is calculated as fish/net night or fish/trap night.
To quantify walleye recruitment in each lake, we employed multiple gears throughout the sampling season including micromesh gillnets, beach seines, and boat electrofishing. In late July/early August, we deployed four 46-m x 1.2-m gillnets with 0.95-cm bar mesh. Sampling locations were evenly distributed along the shoreline and locations were fixed each year. Gillnets were set at night and at depths ranging from 0-5 m. Set duration ranged 1-2 hours to minimize bycatch, thus catches were standardized to age-0 walleyes collected per 10 hours of soak time. In late August, we pulled .24-m long beach seines with 0.64-cm mesh at five sites in each lake. Sites were chosen to represent a variety of habitat types and based on ability to effectively use the seine. Seining sites remained fixed for the duration of the study. Seines were used during daylight hours on each lake. Catch per unit effort was calculated as the number of individuals per seine haul. When water temperatures fell below 21°C (early September), we sampled age-0 walleyes using nighttime boat electrofishing (Wisconsin‐style; AC; 2.0–3.0 amps, 200–350 V, 25% duty cycle, two netters). The entire shoreline of each lake was sampled. Surveys were conducted prior to walleye fingerling stocking. All collected walleye were measured (TL, mm). Catch per unit effort was calculated as the number of age-0 walleyes per meter shoreline.
Removal efforts
In addition to standardized surveys in our experimental lake, in 2018 we began centrarchid removal efforts using a variety of techniques including fyke nets, boat electrofishing, mini-fyke nets, and cloverleaf traps (Fig. 1). Following annual spring fyke net surveys, fyke nets remained in the experimental lake to remove centrarchids. In 2018, we sampled 10 fyke nets from May 14 to June 7 when centrarchid catches started to decline. Due to personnel limitations, in 2019 and 2020 only five fyke nets were used from late spring (May 9, April 30) until late June (June 27, June 25). Additionally, we sampled five mini-fyke nets and 21 cloverleaf traps from late May through mid-August. All gears were emptied every 1-2 days and sites were rotated to maximize centrarchid catches. Collected fish were identified to species and measured (TL, mm). Centrarchid species were retained while other species were returned to McDermott Lake.
NTL Themes
Version Number
1

Lake Mendota, Wisconsin, USA, (Non-Dreissenid) Benthic Macroinvertebrate Abundance, Biomass, and Community Composition 2016-2018

Abstract
We sampled the zoobenthos (macroinvertebrates of the benthos) of Lake Mendota from 2016-2018 to track impacts of invasive zebra mussels (Dreissena polymorpha) which were discovered in Lake Mendota in 2015 and grew exponentially to densities greater than 10,000 m-2 in shallow, rocky habitat by 2018. The data presented here exclude all zebra mussels, which are archived in a separate datset. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. These data also contain some samples opportunistically taken from deeper depths along these transects that do not follow the routine sampling structure. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zebra mussel and phytobenthos data collected according to the same routine sampling structure, for which data is also archived with EDI.
Core Areas
Dataset ID
394
Data Sources
Date Range
-
Methods
We sampled non-zebra mussel benthic macroinvertebrates twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along each of three transects (A-C) running perpendicular to the shore of Lake Mendota. We collected triplicate samples from each site using a 0.625 m-2 circular quadrat and an airlift method with a modified SCUBA tank suction device called an AquaVac. Air was released through a PVC pipe, creating backpressure to lift sediment, which was captured in a 500μm mesh bag and transported in a resealable plastic bag. We chose an airlift method because of difficulty closing Eckman samplers on the hard substrates of rock and zebra mussel druses. Occasionally additional samples were taken with an Eckman, often at deeper depths, for comparing to the main transects and depths sampled with AquaVac or to collect additional material for isotope analysis.<br/>We sampled non-zebra mussel benthic macroinvertebrates twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along each of three transects (A-C) running perpendicular to the shore of Lake Mendota. We collected triplicate samples from each site using a 0.625 m-2 circular quadrat and an airlift method with a modified SCUBA tank suction device called an AquaVac. Air was released through a PVC pipe, creating backpressure to lift sediment, which was captured in a 500μm mesh bag and transported in a resealable plastic bag. We chose an airlift method because of difficulty closing Eckman samplers on the hard substrates of rock and zebra mussel druses. Occasionally additional samples were taken with an Eckman, often at deeper depths, for comparing to the main transects and depths sampled with AquaVac or to collect additional material for isotope analysis.<br/>
Version Number
1

Lake Mendota, Wisconsin, USA, Zebra Mussel Density and Biomass 2016-2018

Abstract
We sampled adult zebra mussels (Dreissena polymorpha) in the benthos of Lake Mendota from 2016-2018 to track the growth of the population following its initial detection in fall 2015. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. Because suitable zebra mussel substrate was limited at these sites, we also selected five 1 m depth, rocky sites (optimal zebra mussel sites) to track density and biomass where colonization was most intense. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zoobenthos and phytobenthos data collected according to the same routine sampling structure, as well as larval zebra mussel (veliger) sampling for which data is also archived with EDI. Biomass data are modeled from lengths of up to 100 individuals that were measured in each sample. Those lengths were fed into Lake Mendota-specific length-to-weight power law equations parameterized by body size measurements (length, width, live weight, wet weight, dry weight, shell weight, shell-free weight, and ash-free dry weight) of 99 mussels collected at different sites across Lake Mendota in 2018.
Core Areas
Dataset ID
393
Date Range
-
LTER Keywords
Methods
We sampled adult zebra mussels twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along each of three transects running perpendicular to shore (A-C). Dominant substrates at transect A were rock at 1 m depth, sand at 3 and 5 m, and muck at 8 and 10 m. At transects B and C, sand was the dominant substrate at 1 and 3 m depth and muck was dominant at 5, 8, and 10m. Significant macrophyte growth was generally absent at all sites in June and occurred mostly at 1, 3, and 5 m sites only at transects A and C. Because most sites lacked hard substrate (rocks, logs, etc.) suitable for zebra mussel colonization, we also sampled five additional rocky 1m depth sites to represent prime zebra mussel habitat.<br/>We sampled adult zebra mussels twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along each of three transects running perpendicular to shore (A-C). Dominant substrates at transect A were rock at 1 m depth, sand at 3 and 5 m, and muck at 8 and 10 m. At transects B and C, sand was the dominant substrate at 1 and 3 m depth and muck was dominant at 5, 8, and 10m. Significant macrophyte growth was generally absent at all sites in June and occurred mostly at 1, 3, and 5 m sites only at transects A and C. Because most sites lacked hard substrate (rocks, logs, etc.) suitable for zebra mussel colonization, we also sampled five additional rocky 1m depth sites to represent prime zebra mussel habitat.<br/>We sampled adult zebra mussels twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along each of three transects running perpendicular to shore (A-C). Dominant substrates at transect A were rock at 1 m depth, sand at 3 and 5 m, and muck at 8 and 10 m. At transects B and C, sand was the dominant substrate at 1 and 3 m depth and muck was dominant at 5, 8, and 10m. Significant macrophyte growth was generally absent at all sites in June and occurred mostly at 1, 3, and 5 m sites only at transects A and C. Because most sites lacked hard substrate (rocks, logs, etc.) suitable for zebra mussel colonization, we also sampled five additional rocky 1m depth sites to represent prime zebra mussel habitat.
Version Number
1

Lake Mendota, Wisconsin, USA, Zebra Mussel Veliger Water Column Density 2016-2019

Abstract
We sampled veliger (larval stage) zebra mussels (Dreissena polymorpha) from 2016-2019.
Zebra mussels are invasive in Lake Mendota and were first detected in November 2015. Samples
were taken at three different sites on Lake Mendota from June to August in 2016, and from
June to November in 2018-2019, using a 0.5 m diameter, 64 micrometer mesh size plankton net
for an 8 m depth tow. This dataset complements adult zebra mussel, zoobenthos, and
phytobenthos data collected during the same time period, for which data is also archived
with EDI.
Core Areas
Dataset ID
392
Data Sources
Date Range
-
Methods
We sampled larval zebra mussels (veligers) using a 64 microm mesh, 0.5 m diameter
plankton net and stored them in 80% ethanol in 200 mL containers at 25degree C for 0-12
weeks until processing. At each site we performed triplicate 8 m depth plankton tows by
pulling a net from 2 m above the lake bottom at the 10 m depth sites of transects A-C
developed for adult zebra mussel collection. We collected samples approximately every 14
days from June to August in 2016, and June to November in 2017-2019. During fall
sampling, poor weather conditions occasionally limited the number of sites or replicates
collected. We also sampled veligers biweekly in 2019 but reduced sampling to one
replicate per site and only sampled at one site after September. Because veligers are
small and difficult to see, enumeration was time consuming. <br/>We sampled larval zebra mussels (veligers) using a 64 microm mesh, 0.5 m diameter
plankton net and stored them in 80% ethanol in 200 mL containers at 25degree C for 0-12
weeks until processing. At each site we performed triplicate 8 m depth plankton tows by
pulling a net from 2 m above the lake bottom at the 10 m depth sites of transects A-C
developed for adult zebra mussel collection. We collected samples approximately every 14
days from June to August in 2016, and June to November in 2017-2019. During fall
sampling, poor weather conditions occasionally limited the number of sites or replicates
collected. We also sampled veligers biweekly in 2019 but reduced sampling to one
replicate per site and only sampled at one site after September. Because veligers are
small and difficult to see, enumeration was time consuming. <br/>We sampled larval zebra mussels (veligers) using a 64 microm mesh, 0.5 m diameter
plankton net and stored them in 80% ethanol in 200 mL containers at 25degree C for 0-12
weeks until processing. At each site we performed triplicate 8 m depth plankton tows by
pulling a net from 2 m above the lake bottom at the 10 m depth sites of transects A-C
developed for adult zebra mussel collection. We collected samples approximately every 14
days from June to August in 2016, and June to November in 2017-2019. During fall
sampling, poor weather conditions occasionally limited the number of sites or replicates
collected. We also sampled veligers biweekly in 2019 but reduced sampling to one
replicate per site and only sampled at one site after September. Because veligers are
small and difficult to see, enumeration was time consuming.
Version Number
1

Lake Mendota, Wisconsin, USA, Phytobenthos Abundance and Community Composition 2016-2018

Abstract
We sampled the phytobenthos (epibenthic periphyton) of Lake Mendota from 2016-2018 to track impacts of invasive zebra mussels (Dreissena polymorpha) which were discovered in Lake Mendota in 2015 and grew exponentially to densities greater than 10,000 m-2 in shallow, rocky habitat by 2018. We sampled along three transects inherited from Karatayev et al. (2013) at five different depths (1, 3, 5, 8, and 10 m) twice a summer (June and August) from 2016-2018. A pared-down version of this routine sampling continued from 2019 onward but is not included here. This dataset complements zebra mussel and zoobenthos data collected according to the same routine sampling structure, for which data is also archived with EDI.
Core Areas
Dataset ID
391
Data Sources
Date Range
-
Methods
We sampled phytobenthos twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along three transects running perpendicular to shore (A-C, Fig. 1). We collected triplicate samples at each site. SCUBA divers retrieved one rock at rock-dominated sites, or a petri dish full of undisturbed sediment at sand- and muck-dominated sites, and transported samples to the surface in a resealable plastic bag. In the laboratory, we scrubbed phytobenthos from rocks with a brush or emptied petri dish contents into a beaker. We separated phytobenthos from inorganic material by adding ~1 L of deionized water, homogenizing the sample, allowing settlement of inorganic material, and decanting the suspended phytobenthos. We kept samples dark and refrigerated until completely processed to prevent cell division after collection. <br/>We sampled phytobenthos twice a summer (early June and late August) from 2016-2018 at five depths (1, 3, 5, 8, and 10m) along three transects running perpendicular to shore (A-C, Fig. 1). We collected triplicate samples at each site. SCUBA divers retrieved one rock at rock-dominated sites, or a petri dish full of undisturbed sediment at sand- and muck-dominated sites, and transported samples to the surface in a resealable plastic bag. In the laboratory, we scrubbed phytobenthos from rocks with a brush or emptied petri dish contents into a beaker. We separated phytobenthos from inorganic material by adding ~1 L of deionized water, homogenizing the sample, allowing settlement of inorganic material, and decanting the suspended phytobenthos. We kept samples dark and refrigerated until completely processed to prevent cell division after collection.
Version Number
1

Madison community science field campaign to assess abundance and distribution of invasive jumping worms.

Abstract
Asian pheretimoid earthworms of the genera Amynthas and Metaphire
(jumping worms) are leading a new wave of co-invasion into
Northeastern and Midwestern states, with potential consequences for
native organisms and ecosystem processes. However, little is known
about their distribution, abundance, and habitat preferences in urban
landscapes – areas which likely influence range expansion via
human-driven spread. We led a participatory field campaign to assess
jumping worm distribution and abundance in Madison, Wisconsin in
September of 2017. By compressing 250 person-hours of sampling effort
into a single day, we quantified presence and abundance of three
jumping worm species across different land-cover types (forest,
grassland, open space, residential lawns and gardens), finding that
urban green spaces differed in invasibility. We show that community
science can be powerful for researching invasive species while
engaging the public in conservation. This approach was particularly
effective here, where broad spatial sampling was required within a
short temporal window.
Core Areas
Dataset ID
387
Date Range
LTER Keywords
Methods
At each study site, teams visually surveyed the area for signs of
jumping worm presence, including live organisms or the characteristic
granular soil signature indicative of their activity. For example, in
a residential yard, participants would walk through the space for
approximately 10 minutes, brushing aside leaf litter and checking
underneath planters or landscaping cloth (where the species are
anecdotally known to congregate) for live earthworms, and examining
garden soil for structural characteristics. Next, earthworms were
censused at three haphazard locations using a 30cm x 30cm quadrat and
a standard mustard extraction (Lawrence and Bowers 2002). Any
suspected jumping worms found were collected and returned to the
laboratory for visual identification following the field campaign. We
identified jumping worms to species (A. tokioensis, A. agrestis, M.
hilgendorfi) when possible (Chang et al. 2016a). Participants also
recorded the presence/absence of any additional (European) earthworm
species observed during sampling.
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NTL Themes
Version Number
1
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