US Long-Term Ecological Research Network

Long-term fish abundance data for Wisconsin Lakes Department of Natural Resources and North Temperate Lakes LTER 1944 - 2012

Abstract
This dataset describes long-term (1944-2012) variations in the relative abundance of fish populations representing nine species in Wisconsin lakes. Data were collected by Wisconsin Department of Natural Resource fisheries biologists as part of routine lake fisheries assessments. Individual survey methodologies varied over space and time and are described in more detail by Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Contact
Core Areas
Creator
Dataset ID
356
Date Range
-
Maintenance
completed
Methods
Fisheries surveys of inland lakes and streams in Wisconsin have been conducted by the Wisconsin Department of Natural Resources (WDNR) professionals and its predecessor the Wisconsin Conservation Department for >70 y. Standard fyke net and boat electrofishing surveys tend to dominate the fisheries surveys and data collected. Most fyke net data on certain species (e.g., Walleye Sander vitreus and Muskellunge Esox masquinongy) originates from annual spring netting surveys following ice-out. These data are used for abundance estimates, mark and recapture surveys for estimating population sizes, and egg-take procedures for the hatcheries. Boat-mounted boom and mini-boom electrofishing surveys became increasingly common in the late 1950s and 1960s. Boat electrofishing surveys have typically been conducted during early summer months (May and June), but some electrofishing survey data are also collected in early spring as part of walleye and muskellunge mark-recapture surveys. Summer fyke netting surveys have been collected more sporadically over time, but were once more commonly used as a panfish survey methodology. Surveys were largely non-standardized. Thus, future users and statistical comparisons utilizing these data should acknowledge the non-standard nature of their collection. More in-depth description of these data can be found in Rypel, A. et al., 2016. Seventy-Year Retrospective on Size-Structure Changes in the Recreational Fisheries of Wisconsin. Fisheries, 41, pp.230-243. Available at: http://afs.tandfonline.com/doi/abs/10.1080/03632415.2016.1160894
Version Number
5

Cascade Project at North Temperate Lakes LTER Core Data Phytoplankton 1984 - 2015

Abstract
Data on epilimnetic phytoplankton from 1984-2015, determined by light microscopy from pooled Van Dorn samples at 100 percent, 50 percent, and 25 percent of surface irradiance. St. Amand (1990) and Cottingham (1996) describe the counting protocols in detail. Samples after 1995 were counted by Phycotech Inc. (http://www.phycotech.com). Sampling Frequency: varies; Number of sites: 5
Dataset ID
353
Date Range
-
Methods
Samples counted prior to 1996 were assigned one taxon name with all taxonomic information. This taxon name was split into distinct columns of genus, species and description for archival as best possible. Samples from 2013-2015 were sent to Phycotech inc. (http://www.phycotech.com/) to be counted.
Version Number
16

Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format

Abstract
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Core Areas
Dataset ID
349
Date Range
-
Methods
Protocol available in methods section of: http://msphere.asm.org/content/2/3/e00169-17
Prior to collection, water temperature and dissolved oxygen concentrations are measured using a YSI 550a. The ranges of the epilimnion and hypolimnion are determined based on the location of the thermocline (where temperature/oxygen is changing the fastest). The two layers are collected separately in 1 meter increments using an integrated water column sampler. Water samples are taken back to the lab, shaken thoroughly, and filtered via peristaltic pump through 0.22 micron filters (Pall Supor). Filters are temporarily stored at -20C after collection and then transferred to -80C after transport on dry ice from Trout Lake Station to UW-Madison. Nutrient samples are collected bi-weekly following standard LTER protocols. DNA is extracted from filters using a FASTDNA SpinKit for Soil with minor modifications. (In cases of low yield or specialized sequencing methods, a phenol-chloroform extraction is used instead). The protocol for sequencing and analysis of data varies by year and by sub-project.
Version Number
4

North Temperate Lakes LTER Bythotrephes longimanus spiny water flea population monitoring in Wisconsin and Minnesota 2009 - 2014

Abstract
Three data tables are included describing population dynamics for Bythotrephes longimanus, spiny water flea, in Southern Wisconsin during invasion. General monitor took place in Lake Mendota, Lake Monona, Lake Waubesa, Lake Kegonsa, Stormy Lake, Gile Flowage, Lake Gogebic.Accompanying Bythotrephes morphological measurements from Lake Mendota monitoring efforts in 2011 and 2012. Included are individual measurements of body morphology and reproductive status for ~2,500 <em>Bythotrephes </em>collected from Lake Mendota in 2011 and 2012.Sediment cores from Lake Mendota were analyzed for spiny water flea evidence with age of sediment estimated.
Contact
Core Areas
Dataset ID
342
Date Range
-
Maintenance
complete
Methods
general monitoring for spiny water flea:
The dataset contains collected Bythotrephes longimanus monitoring efforts from 8 invaded lakes in Wisconsin that took place over the course of 2009 through 2014 using a zooplankton net. Monitoring efforts were conducted to 1) obtain more accurate estimates of Bythotrephes densities using a more appropriately sized net (50-cm diameter over 30-cm diameter) and 2) obtain detailed demographic measurements of Bythotrephes morphology and reproduction in each lake. Here only Bythotrephes densities are included.
The majority of samples occurred at a lakes deep hole with a 50-cm diameter and 150-micron mesh zooplankton net. Nets are lowered to 2 m off of the lake bottom before being towed to the surface. Samples are processed in their entirety
Exceptions to this are those at sites containing “LTER” (e.g., site IDs LTER-DH and LTER-MB) in their ID which were samples taken according to the Southern Lakes LTER zooplankton collection protocol with a 30-cm and 83-micron mesh. Other exceptions include sites outside the deep hole of the lake (site ID 5m = 5m lake depth north of the Center for Limnology on Lake Mendota; CFL = 15m lake depth north of the Center for Limnology; DH = deep hole but specific to Lake Mendota; MB = 15m lake depth southwest of Maple Bluffs in Madison on Lake Mendota; MO.5m = a 5m lake depth site in Lake Monona; MO.Y = 5m lake depth site at the mouth of the Yahara River on Lake Monona; TL = 15m lake depth west of Tenney Locks in Madison on Lake Mendota; WS = 15m site in northwestern basin of Lake Mendota, east of Picnic Point; WP = 5m site south of Warner Park on Lake Mendota). Several tows were taken using a 200m oblique (i.e., horizontal) net tow with the 50-cm diameter net (DH-ObliqueTow). Efforts in Southern Wisconsin were led by Jake Walsh while efforts in Northern Wisconsin were led by Carol Warden (site ID = CW), Pam Montz (site ID = PM), Sam Christel (site ID = SC), Sam Oliver (site ID = SM), as well as a researcher with initials (site ID) “EM”.
Version Number
8

LTREB Lake Myvatn Predation experiments at Myvatn, Iceland during 2009 and 2011

Abstract
Changes in one prey species&#39; density can indirectly affect the abundance of another prey species if a shared predator eats both species leading to positive or negative indirect effects. In some cases, indirect effects may occur when prey move into a habitat, such as when riparian predator populations grow in response to adult aquatic insects and increase predation on terrestrial prey. However, predators could instead switch to aquatic insects or become satiated, reducing predation on terrestrial prey. To determine the net indirect effect of aquatic insects on terrestrial arthropods via generalist spider predators, we conducted a field experiment using enclosures on the shoreline of an Icelandic lake with numerous aquatic midges. Midge abundance and wolf spider density were altered to mimic midge influx and a wolf spider numerical response. At all predator densities, the presence of midges decreased rates of predation on terrestrial prey. When midges were absent, predation was 30percent greater at high spider density. But when midges were present, predation of sentinel prey was equal across spider densities, negating the influence of increased predator density. In lab mesocosms, prey survivorship increased greater or equal 50percent where midges were present and rapidly saturated; the addition of 5, 20, 50 and 100 midges equivalently reduced spider predation, supporting predator distraction rather than satiation as the root cause. Our results demonstrate a strong positive indirect effect of midges, and broadly support the concept that predator responses to alternative prey are a major influence on the magnitude and direction of predator-mediated indirect effects.
Contact
Dataset ID
310
Date Range
-
Metadata Provider
Methods
Study SystemWe examined the potential indirect effect of aquatic insects on terrestrial arthropods at Lake Myvatn in northeast Iceland (65degree36 N, 17degree00 W). Lake Myvatn is a large, shallow lake (mean depth 3 m) within a geologically active region in northeastern Iceland (Thorarinsson 1979) whose subsurface springs rich in nutrients combine with the shallow water and long day lengths to promote high algal productivity (Einarsson et al. 2004, Thorbergsdottir and Gislason 2004). This primary production supports large populations of aquatic insects, primarily chironomid midges (Diptera: Chironomidae; (Lindegaard and Jónasson 1979). After emerging from the water as adults, midges congregate in large swarms over land near shore to mate. Lake Myvatn is historically known to produce abundant midge populations (Lindegaard and Jónasson 1979, Einarsson et al. 2002), and midge density varies throughout the summer season with peaks around late May and late July (Einarsson et al. 2002, Dreyer et al. 2015). On land, terrestrial arthropod predators, including wolf spiders in the genus Pardosa, consume midges (Gratton et al. 2008) and spider abundance is higher where midges are more common (Gratton et al. 2008, Dreyer et al. 2012).
Version Number
18

Microbial Observatory at North Temperate Lakes LTER Spatial and temporal cyanobacterial population dynamics in Lake Mendota 2009 - 2011

Abstract
Toxic cyanobacterial blooms threaten freshwaters worldwide but have proven difficult to predict because the mechanisms of bloom formation and toxin production are unknown, especially on weekly time scales. Water quality management continues to focus on aggregated metrics, such as chlorophyll and total nutrients, which may not be sufficient to explain complex community changes and functions such as toxin production. For example, nitrogen (N) speciation and cycling play an important role, on daily time scales, in shaping cyanobacterial communities because declining N has been shown to select for N fixers. In addition, subsequent N pulses from N<sub>2</sub> fixation may stimulate and sustain toxic cyanobacterial growth. Herein, we describe how rapid early summer declines in N followed by bursts of N fixation have shaped cyanobacterial communities in a eutrophic lake (Lake Mendota, Wisconsin, USA), possibly driving toxic <em>Microcystis</em> blooms throughout the growing season. On weekly time scales in 2010 and *2011, we monitored the cyanobacterial community in a eutrophic lake using the phycocyanin intergenic spacer (PC-IGS) region to determine population dynamics. In parallel, we measured microcystin concentrations, N<sub>2</sub> fixation rates, and potential environmental drivers that contribute to structuring the community.
Core Areas
Dataset ID
299
Date Range
-
Maintenance
completed
Metadata Provider
Methods
Field sample collection and processingAt each location, temperature, dissolved oxygen (DO), and pH were collected at 1 m increments from the surface to the maximum depth (YSI 556MPS). Photic zone depth was defined at 1percent of photosynthetically active radiation (PAR) as measured using a PAR sensor (LiCor 192SA). Integrated photic zone samples were then collected using a weighted 2-inch diameter polypropylene tube. Samples for DNA, nutrients, toxins, and pigment analyses were collected in acid-washed, sterile bottles, (rinsed three times with in situ water before collection) and stored on ice until further processing.Once transported back to the lab, samples were immediately processed. For dissolved reactive phosphorus (DRP), total dissolved phosphorus (TDP), total dissolved nitrogen (TDN), nitrate, and nitrite, 100 mL of water was filtered through a Whatman glass fiber filter (GForF) and frozen at -20 degreeC. For TP and total nitrogen (TN), HCl was added to 100mL of sample to a final concentration of 0.1percent and stored at -20 degreeC. Ammonium samples were immediately measured to avoid oxidation during freezing. Chlorophyll-a and phycocyanin samples were collected onto GForF filters and stored in black tubes at -20 degreeC. For community analysis (DNA), samples were filtered onto 0.2 &micro;m polyethersulfone membrane filters (Supor-200; Pall Corporation) and frozen at -20 degreeC until extraction. 20 mL of unfiltered water was preserved in formalin (3percent final concentration) and stored at room temperature in the dark for microscopy. An additional 50 mL of unfiltered water was stored at -20 degreeC for toxin analysis.Analytical measurementsDRP was measured by the ascorbic acid-molybdenum blue method 4500 P E (Greenberg et al. 1992). Ammonium was measured spectrophotometrically (Solórzano 1969). Nitrate and nitrite were measured individually using high-performance liquid chromatography (HPLC) (Flowers et al. 2009). TPorTDP and TNorTDN were digested as previously described (White et al. 2010), prior to analysis as for DRP and nitrate. For TDN and TN, the resulting solution was oxidized completely to nitrate and was measured via HPLC as above. Nitrate, nitrite, and ammonium were summed and reported as dissolved inorganic nitrogen (DIN).Phycocyanin was extracted in 20 mM sodium acetate buffer (pH 5.5) following three freeze-thaw cycles at -20 &ordm;C and on ice, respectively. The extract was centrifuged and then measured spectrophotometrically at 620 nm with correction at 650 nm (Demarsac and Houmard 1988). Chlorophyll-a (Chl-a) was extracted overnight at -20 &ordm;C in 90percent acetone and then measured spectrophotometrically with acid correction (Tett et al. 1975).For toxin analysis, whole water samples were lyophilized, resuspended in 5percent acetic acid, separated by solid phase extraction (SPE; Bond Elut C18 column, Varian), and eluted in 50percent methanol as previously described (Harada et al. 1988). Microcystin (MC) variants of leucine (L), arginine (R), and tyrosine (Y) were detected and quantified at the Wisconsin State Lab of Hygiene (SLOH) using liquid chromatography electrospray ionization tandem mass spectroscopy (API 3200, MSorMS) after separation by HPLC (Eaglesham et al. 1999). We report only MCLR concentrations since MCYR and MCRR were near the limit of quantification for the sampling period (0.01 &micro;g L-1).In situ N2 fixation measurementsN2 fixation rates were measured, with some modifications, following the acetylene reduction assay (Stewart et al. 1967). A fresh batch of acetylene was generated each day before sampling by combing 1 g of calcium carbide (Sigma Aldrich 270296) with 100 mL ddH2O. Following sample collection, 1 L of water was concentrated by gentle filtration onto a 47 mm GForF filter in the field. The filter was then gently washed into a 25 mL serum bottle using the lake water filtrate (final volumes 10 mL aqueous, 15 mL gas). Samples were spiked with 1 mL of acetylene gas and incubated in situ for two hours. The assay was terminated with 5percent final concentration trichloracetic acid and serum bottles were transported back to the lab. For each sampling period, rates were controlled and corrected for using a series of the following incubated acetylene blanks: 1) 1 mL of acetylene in filtrate alone, 2) 1 mL of acetylene in a killed sample, and 3) 1 mL of acetylene in ddH2O. Ethylene formed was measured by a gas chromatograph (GC; Shimadzu GC-8A) equipped with a flame ionization detector (FID), Porapak N column (80or100 mesh, 1or8OD x 6), and integrator (Hewlett Packard 3396) with N2 as the carrier gas (25 mL min-1 flow rate). Molar N2 fixation rates were estimated using a 1:4 ratio of N2 fixed to ethylene formed (Jensen and Cox 1983). All N2 fixation values are reported as integrated photic zone rates of &micro;g N L-1 hr-1.DNA extraction and processing of PC-IGS fragmentDNA was extracted from frozen filters using a xanthogenate-phenol-chloroform protocol previously described (Miller and McMahon 2011). For amplification of the phycocyanin intergenic spacer (PC-IGS) region, we used primers PCalphaR (5-CCAGTACCACCAGCAACTAA-3) and PCbetaF (5-GGCTGCTTGTTTACGCGACA-3, 6-FAM-labelled) and PCR conditions that were previously described (Neilan et al. 1995). Briefly, each 50 &micro;l reaction mixture contained 5 &micro;l of 10X buffer (Promega, Madison, WI), 2.5 &micro;l of dNTPs (5 mM), 2 &micro;l of forward and reverse primers (10 &micro;M), 2 &micro;l of template DNA, and 0.5 &micro;l of Taq DNA polymerase (5 U &micro;l-1). Following precipitation with ammonium acetate and isopropanol, the DNA pellet was resuspended in ddH2O and digested for 2 hrs at 37 &ordm;C using the MspI restriction enzyme, BSA, and Buffer B (Promega, Madison, WI). The digested product was precipitated and then resuspended in 20 &micro;L of ddH2O. 2 &micro;L of final product was combined with 10 &micro;L of formamide and 0.4 &micro;L of a custom carboxy-x-rhodamine (ROX) size standard (BioVentures, Inc).Cyanobacterial PC-IGS community fingerprinting and cell countsWe analyzed the cyanobacterial community using an automated phycocyanin intergenic spacer analysis (APISA) similar to the automated ribosomal intergenic spacer analysis (ARISA) previously described (Yannarell et al. 2003). Briefly, this cyanobacterial-specific analysis exploits the variable PC-IGS region of the phycocyanin operon (Neilan et al. 1995). Following MspI digestion, the variable lengths of the PC-IGS fragment can be used to identify subgenus level taxonomic units of the larger cyanobacterial community (Miller and McMahon 2011). The MspI fragments were sized using denaturing capillary electrophoresis (ABI 3730xl DNA Analyzer; University of Wisconsin Biotechnology Center (UWBC)). For each sample, triplicate electropherogram profiles were analyzed using GeneMarker&reg; (SoftGenetics) software v 1.5. In addition, a script developed in the R Statistics Environment was used to distinguish potential peaks from baseline noise (Jones and McMahon 2009, Jones et al. 2012). Relative abundance data output from this script were created using the relative proportion of fluorescence each peak height contributed per sample. Aligned, overlapping peaks were binned into subgenus taxonomic units (Miller and McMahon 2011). These taxa were named based on the genus and base pair length of the PC-IGS fragment identified (e.g. For Mic215, Mic = Microcystis and 215 = 215 base pair fragment). Fragment lengths were matched to an in silico digested database of PC-IGS sequences using the Phylogenetic Assignment Tool (https:ororsecure.limnology.wisc.eduortrflpor).The NTL-LTER program collects biweekly phytoplankton samples between April and September for cell counts and detailed descriptions of the field and laboratory protocols are available online at http:ororlter.limnology.wisc.edu. When indicated, biomass has been converted to mg L-1 using the biovolume calculated during the cell count process and assuming a density equivalent to water.
Version Number
22

Microbial Observatory at North Temperate Lakes LTER Time series of bacterial community dynamics in Lake Mendota 2000 - 2009

Abstract
With an unprecedented decade-long time series from a temperate eutrophic lake, we analyzed bacterial and environmental co-occurrence networks to gain insight into seasonal dynamics at the community level. We found that (1) bacterial co-occurrence networks were non-random, (2) season explained the network complexity and (3) co-occurrence network complexity was negatively correlated with the underlying community diversity across different seasons. Network complexity was not related to the variance of associated environmental factors. Temperature and productivity may drive changes in diversity across seasons in temperate aquatic systems, much as they control diversity across latitude. While the implications of bacterioplankton network structure on ecosystem function are still largely unknown, network analysis, in conjunction with traditional multivariate techniques, continues to increase our understanding of bacterioplankton temporal dynamics.
Core Areas
Dataset ID
298
Date Range
Maintenance
completed
Metadata Provider
Methods
Surface water samples were collected from Lake Mendota, WI, USA, and analyzed by automated ribosomal intergenic spacer analysis as described previously (Shade et al., 2007). From 2000 to 2009, a total of 34 spring, 53 summer and 34 autumn observations were made. Thirty-two environmental variables were collected at the same location by the North Temperate Lakes Long Term Ecological Research program (lter.limnology.wisc.edu)
Short Name
MEMOTY
Version Number
18

Microbial Observatory at North Temperate Lakes LTER Mendota Six Years Bacterial Community Composition 2000 - 2005

Abstract
We investigated patterns of intra- and interannual change in pelagic bacterial community composition (BCC), assessed using automated ribosomal intergenic spacer analysis) over six years in eutrophic Lake Mendota, Wisconsin. A regular phenology was repeated across years, implying that freshwater bacterial communities are more predictable in their dynamics than previously thought. Seasonal events, such as water column mixing andtrends in water temperature, were most strongly related to BCC variation. Communities became progressively less similar across years between the months of May and September, when the lake was thermally stratified. Dissolved oxygen and nitrate + nitrite concentrations were highly correlated to BCC change within and across seasons. The relationship between BCC and seasonal drivers suggests that trajectories of community change observed over long time series will reflect large-scale climate variation.
Core Areas
Dataset ID
293
Data Sources
Date Range
-
Maintenance
complete
Metadata Provider
Methods
Sampling Frequency: bi-weekly during ice-off from 2000 to 2005Total number of observations: 82Number of sites: 1 site over the deep hole of the lake (Lake Mendota, 89degree 24 W long, 43degree 06 N lat)Sampling techniques: integrated 0-12 mNomenclature:Lake name is ME. Sample IDs are ME-date with date = MM-DD-YY.DNA extraction protocol: FastPrep DNA extraction kitBinning protocol: Manual in GeneScanorGenotyperCapillary Instrument (from Biotech Center): ABI 3700PCR DNA standardization protocol: By volume of lake water filteredPCR thermocycler protocol:RISA30x protocol: 2 min 94C, [35s. Denature 94C, 45 s. Annealing 55C, 2 min Extension 72C (rep 30x)], 2 min extension 72C.Analyses performed other than ARISA:Additional comments:Raw ABI files are in two batchesBatch 1: Duplicate samples from 2000-2004 with nomenclature basically the same as the sample IDs plus the replicate number, such as ME 2-28-02 rep1. Note that the month is not 2-digit unless it has two digits.Batch 2: Duplicate samples from 2000-2005 with nomenclature as ME-date where date is DD-month-YY and the replicate number, such as ME 27Apr00 rep1.NOTE: We are not sure which samples were used for the final analysis. They must have been a blend of the two batches. Contact Ashley Shade (ashley17061atgmail.com) for clarification if needed.
Short Name
ME0005
Version Number
17

Microbial Observatory at North Temperate Lakes LTER Six Bogs Microbial Communities 2009

Abstract
Population dynamics are influenced by drivers acting from outside and from within an ecosystem. Extrinsic forces operating over broad spatial scales can impart synchronous behavior to separate populations, while internal, system-specific drivers often lead to idiosyncratic behavior. Here we demonstrate synchrony in community-level dynamics among phytoplankton and bacteria in six north temperate humic lakes. The influence of regional meteorological factors explained much of the temporal variability in the phytoplankton community, and resulted in synchronous patterns of community change among lakes. Bacterial dynamics, in contrast, were driven by system-specific interactions with phytoplankton. Despite the importance of intrinsic factors for determining bacterial community composition and dynamics, we demonstrated that biological interactions transmitted the signal of the regional extrinsic drivers to the bacterial communities, ultimately resulting in synchronous community phenologies for bacterioplankton communities as well. This demonstrates how linkages between the components of a complex biological system can work to simplify the dynamics of the system and implies that it may be possible to predict the behavior of microbial communities responsible for important biogeochemical services in the landscape.
Core Areas
Dataset ID
292
Date Range
-
Metadata Provider
Methods
See protocols of North Temperate Lakes Microbial ObservatorySampling Techniques: Integrated epilimnionDNA extraction protocol: FastPrep DNA extraction kitBinning protocol: ARISA_v4.2.RCapillary Instrument (from Biotech Center): ABI 3730xLPCR DNA standardization protocol: By volume of DNA extract (1 ul per reaction)PCR thermocycler protocol: RISAASH protocol: 2 min at 94 C, [30 s at 94, 45 s at 55, 1 m at 72, (Repeat 29X)], 1 m at 72, Hold at 4
NTL Keyword
Short Name
6BOG03
Version Number
21

LTREB Kalfastrond Peninsula Experiment (KAL) Midge Counts at Lake Myvatn 2008-2011

Abstract
A cross ecosystem resource blocking experiment was conducted on the Kalfastrond peninsula, known as the KAL experiment or KAL midge blocking experiment, at Lake Myvatn to determine the influence of an aquatic resource on a terrestrial food web over time. A manipulative field experiment was used in conjunction with a stable isotope analysis to examine changes in terrestrial arthropod food webs in response to the midge subsidy. Cages were established at 2 by 2 meter plots in 6 blocks spread across the site. Each block included 3 treatment levels, an open control plot, a full exclusion cage and a partial exclusion cage, for a total of 18 experimental plots. Midge exclusion cages were designed to prevent midges from entering plots with such cages. Control open pit midge cages were set as a control which allowed complete access to all arthropods. Partial midge exclusion cages were designed and used to examine any effects of cages themselves on terrestrial responses while minimally affecting midge inputs into the plots and arthropod movement. All cages were set at the middle to end of May to the beginning of August in each year, the period corresponding to the active growing season of plants and the flight activity of midges at this site. Midge activity was measured in all plots to document changes in midge abundance over the course of a season and between years and to assess the degree to which cages excluded midges.Midge abundance in the plots was continuously measured using passive aerial infall traps. Midges from infall traps were counted and identified to morphospecies, where the small species is Tanytarsus gracilentus and the large species is Chironomus islandicus. Some arthropods were only identified to the family level Simuliidae, and other arthropods were lumped in a category named others. If the infall trap contained hundreds to thousands of a particular midge species a subsample for each species was performed to estimate the number of midges trapped. These data are the results of the midge counts from the infall traps.
Contact
Core Areas
Dataset ID
284
Date Range
-
Maintenance
Ongoing
Metadata Provider
Methods
I. Field MethodsThe site where this manipulative field experiment was conducted on the Kalfastrond peninsula at Lake Myvatn is approximately 150 meters long and 75 meters wide. The vegetation consists of grasses Deschampsia spp., Poa spp., and Agrostis spp.), sedges (Carex spp.), and forbs (Ranunculus acris, Geum rivale,and Potentilla palustris). The experimental midge exclusions occurred from the middle or end of May to the beginning of August in each year, the period corresponding to the active growing season of plants and the flight activity of midges at this site. 2 by 2 meter plots were established in 6 blocks spread across the site. Each block included 3 treatment levels, an open control plot, a full exclusion cage and a partial exclusion cage, for a total of 18 experimental plots. Control plots were open to allow complete access to all arthropods. Experimental midge exclusion cages were 1 meter high and constructed from white PVC tubing affixed to rebar posts on each corner of the plot, Plate 1. Full exclusion cages were entirely covered with white polyester netting, 200 holes per square inch, Barre Army Navy Store, Barre VT, USA, to prevent midges from entering the plot. The mesh netting completely enclosed the 2 by 2 by 1 meter frame to prevent flying insects from entering, however the mesh was not secured to the ground in order to allow non flying,ground crawling, arthropods to freely enter and exit the cages. Partial exclusion cages had one 0.5 meter strip of mesh stretched around the outside of the frame and another 0.75 meter strip draped over the top. Partial cages were designed to examine any effects of cages themselves on terrestrial responses while minimally affecting midge inputs into the plots and arthropod movement.The partial exclusion treatment was discontinued in 2011. Each plot contains a pitfall and an infall trap that are continuously sampled during the summer, while the cages are up. Vacuum samples were taken from the plots about once per month in 2008 through 2010 and only once per summer for subsequent summers.Midge activity was measured in all plots to document changes in midge abundance over the course of a season and between years and to assess the degree to which cages excluded midges. Midge abundance in the plots was continuously measured using passive aerial infall traps consisting of a 1000 milliliter clear plastic cup, 95 square centimeter opening, attached to a post 0.5 meters high and filled with 250 milliliters of a 1 to 1 ethylene glycol to water solution and a small amount of unscented detergent to capture and kill insects that alighted upon the surface. Infall traps were emptied about every 10 days.II. AnalysisMidges were counted and identified to morphospecies, small and large. The midge (Diptera,Chrionomidae) assemblage at Myvatn is dominated by two species,Chironomus islandicus (Kieffer)(large, 1.1 mg dw) and Tanytarsus gracilentus(Holmgren)(small, 0.1 mg dw), together comprising 90 percent of total midge abundance (Lindegaard and Jonasson 1979). First, the midges collected in the infall traps were spread out in trays, and counted if there were only a few. Some midges were only identified to the family level of Simuliidae,and other arthropods were counted and categorized as the group, others. Arthropods only identified to the family level Simuliidae or classified as others were not dually counted as Chironomus islandicus or Tanytarsus gracilentus. If there were many midges, generally if there were hundreds to thousands, in an infall trap,subsamples were taken. Subsampling was done using plastic rings that were dropped into the tray. The rings were relatively small compared to the tray, about 2 percent of the area of a tray was represented in a ring. The area inside a ring and the total area of the trays were also measured. Note that different sized rings and trays were used in subsample analysis. These are as follows, Trays, small (area of 731 square centimeters), large1 (area of 1862.40 square centimeters), and large2 (area of 1247 square centimeters). Rings, standard ring (diameter of 7.30 centimeters, subsample area is 41.85 square centimeters) and small ring (diameter of 6.5 centimeters, subsample area is 33.18 square centimeters). A small ring was only used to subsample trays classified as type large2.The fraction subsampled was then calculated depending on the size of the tray and ring used for the subsample analysis. If the entire tray was counted and no subsampling was done then the fraction subsampled was assigned a value of 1.0. If subsampling was done the fraction subsampled was calculated as the number of subsamples taken multiplied by the fraction of the tray that a subsample ring area covers (number of subsamples multiplied by (ring area divided by tray area)). Note that this is dependent on the tray and ring used for subsample analysis. Finally, the number of midges in an infall trap accounting for subsampling was calculated as the raw count of midges divided by the fraction subsampled (raw count divided by fraction subsampled).Other metrics such as total insects in meters squared per day, and total insect biomass in grams per meter squared day can be calculated with these data. in addition to the estimated average individual midge masses in grams, For 2008 through 2010 average midge masses were calculated as, Tanytarsus equal to .0001104 grams, Chironomus equal to .0010837 grams. For 2011 average midge masses were, Tanytarsus equal to .000182 grams, Chironomus equal to .001268 grams.
Version Number
15
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