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 Biological Limnology at Lake Myvatn 2012-current

Abstract
These data are part of a long-term monitoring program in the central part of Myvatn that represents the dominant habitat, with benthos consisting of diatomaceous ooze. The program was designed to characterize import benthis and pelagic variables across years as midge populations varied in abundance. Starting in 2012 samples were taken at roughly weekly inervals during June, July, and August, which corresponds to the summer generation of the dominant midge,<em>Tanytarsus gracilentus</em>.
Creator
Dataset ID
296
Date Range
-
Maintenance
Ongoing
Metadata Provider
Methods
Benthic Chlorophyll Field sampling (5 samples) (2012, 2013)1. Take 5 cores from the lake2. Cut the first 0.75 cm (1 chip) of the core with the extruder and place in deli container. Label with date and core number.3. Place deli containers into opaque container (cooler) and return to lab. This is the same sample that is used for the organic matter analysis.In 2014, the method for sampling benthic chlorophyll changed. The calculation of chlorophyll was changed to reflect the different area sampled. Below is the pertinent section from the methods protocols. Processing after the collection of the sample was not changed.Take sediment samples from the 5 cores collected for sediment characteristics. Take 4 syringes of sediment with 10mL syringe (15.96mm diameter). Take 4-5cm of sediment. Then, remove bottom 2cm and place top 2cm in the film canister.Filtering1. Measure volume of material in deli container with 60mL syringe and record.2. Homogenize and take 1mL sample with micropipette. The tip on the micropipette should be cut to avoid clogging with diatoms. Place the 1mL sample in a labeled film canister. Freeze sample at negative 20 degrees Celsius unless starting methanol extraction immediately.3. Add 20mL methanol. This methanol can be kept cool in the fridge, although then you will need a second bottle of methanol for the fluorometer. Shake for 5 sec.4. After 6-18 hours, shake container for 5 sec.Fluorometer1. Allow the film canisters to sit at room temperature for approximately 15 min to avoid excessive condensation on the glass tubes. Shake tubes for 5 sec after removing from fridge but then be careful to let them settle before removing sample.2. Record the sample information for all of the film canisters on the data sheet.3. Add 4mL of sample to a 13x100mL glass tube.4. Insert the sample into the fluorometer and record the reading in the Fluor Before Acid column. The sample reading should be close to one of the secondary solid standards (42ug/L or 230ug/L), if not, dilute the sample to within 25 per cent of the secondary solid standards (30-54ug/L or 180-280ug/L). It is a good idea to quickly check 2mL of a sample that is suspected to be too high to get an idea if other samples may need to be diluted. If possible, read the samples undiluted.5. If a sample needs to be diluted, use a 1000 microLiter pipette and add 2mL of methanol to a tube followed by 2mL of undiluted sample. Gently invert the tube twice and clean the bottom with a paper towel before inserting it into the fluorometer. If the sample is still outside of the ranges above, combine 1 mL of undiluted sample with 3 mL of methanol. Be sure to record the dilution information on the data sheet.6. Acidify the sample by adding 120microLiters of 0.1 N HCl (30microLiters for every one mL of sample). Then gently invert the sample and wait 90 seconds (we used 60 seconds in 2012, the protocol said 90) before putting the sample into the fluorometer and recording the reading in the Fluor After Acid column. Be sure to have acid in each tube for exactly the same amount of time. This means doing one tube at a time or spacing them 30-60 seconds apart.7. Double check the results and redo samples, which have suspicious numbers. Make sure that the after-acidification values make sense when compared to the before acidification value (the before acid/after acid ratio should be approximately the same for all samples).Clean up1. Methanol can be disposed of down the drain as long as at least 50 times as much water is flushed.2. Rinse the film canisters and lids well with tap water and scrub them out with a bottle brush making sure to remove any remaining filter paper. Give a final rinse with distilled water. Pelagic Chlorophyll Field sampling (5 samples)1. Take 2 samples at each of three depths, 1, 2, and 3m with Arni&rsquo;s zooplankton trap. For the 1m sample, drop the trap to the top of the chain. Each trap contains about 2.5L of water when full. 2. Empty into bucket by opening the bottom flap with your hand.3. Take bucket to lab.Filtering1. Filter 1L water from integrated water sample (or until the filter is clogged) through the 47 mm GF/F filter. The pressure used during filtering should be low ( less than 5 mm Hg) to prevent cell breakage. Filtering and handling of filters should be performed under dimmed lighting.2. Remove the filter with forceps, fold it in half (pigment side in), and put it in the film canister. Take care to not touch the pigments with the forceps.3. Add 20mL methanol. This methanol can be kept cool in the fridge, although then you will need a second bottle of methanol for the fluorometer. Shake for 5 sec. and place in fridge.4. After 6-18 hours, shake container for 5 sec.5. Analyze sample in fluorometer after 24 hours.Fluorometer1. Allow the film canisters to sit at room temperature for approximately 15 min to avoid excessive condensation on the glass tubes. Shake tubes for 5 sec after removing from fridge but then be careful to let them settle before removing sample.2. Record the sample information for all of the film canisters on the data sheet.3. Add 4mL of sample to a 13x100mL glass tube.4. Insert the sample into the fluorometer and record the reading in the Fluor Before Acid column. The sample reading should be close to one of the secondary solid standards (42ug/L or 230ug/L), if not, dilute the sample to within 25 percent of the secondary solid standards (30-54ug/L or 180-280ug/L). It is a good idea to quickly check 2mL of a sample that is suspected to be too high to get an idea if other samples may need to be diluted. If possible, read the samples undiluted.5. If a sample needs to be diluted, use a 1000uL pipette and add 2mL of methanol to a tube followed by 2mL of undiluted sample. Gently invert the tube twice and clean the bottom with a paper towel before inserting it into the fluorometer. If the sample is still outside of the ranges above, combine 1 mL of undiluted sample with 3 mL of methanol. Be sure to record the dilution information on the data sheet.6. Acidify the sample by adding 120 microLiters of 0.1 N HCl (30 microLiters for every one mL of sample). Then gently invert the sample and wait 90 seconds (we used 60 seconds in 2012, the protocol said 90) before putting the sample into the fluorometer and recording the reading in the Fluor After Acid column. Be sure to have acid in each tube for exactly the same amount of time. This means doing one tube at a time or spacing them 30-60 seconds apart.7. Double check the results and redo samples, which have suspicious numbers. Make sure that the after-acidification values make sense when compared to the before acidification value (the before acid/after acid ratio should be approximately the same for all samples).Clean up1. Methanol can be disposed of down the drain as long as at least 50 times as much water is flushed.2. Rinse the film canisters and lids well with tap water and scrub them out with a bottle brush making sure to remove any remaining filter paper. Give a final rinse with distilled water. Pelagic Zooplankton Counts Field samplingUse Arni&rsquo;s zooplankton trap (modified Schindler) to take 2 samples at each of 1, 2, and 3m (6 total). For the 1m sample, drop the trap to the top of the chain. Each trap contains about 2.5L of water when full. Integrate samples in bucket and bring back to lab for further processing.Sample preparation in lab1. Sieve integrated plankton tows through 63&micro;m mesh and record volume of full sample2. Collect in Nalgene bottles and make total volume to 50mL3. Add 8 drops of lugol to fix zooplankton.4. Label bottle with sample date, benthic or pelagic zooplankton, and total volume sieved. Samples can be stored in the fridge until time of countingCounting1. Remove sample from fridge2. Sieve sample with 63 micro meter mesh over lab sink to remove Lugol&rsquo;s solution (which vaporizes under light)3. Suspend sample in water in sieve and flush from the back with squirt bottle into counting tray4. Homogenize sample with forceps or plastic pipette with tip cut off5. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted as well.6. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridgeSubsamplingIf homogenized original sample contains more than 500 individuals in the first line of counting tray, you may subsample under the following procedure.1. Return original sample to Nalgene bottle and add water to 50mL2. Homogenize sample by swirling Nalgene bottle3. Collect 10mL of zooplankton sample with Hensen-Stempel pipette4. Empty contents of Hensen-Stempel pipette into large Bogorov tray5. Homogenize sample in tray with forceps or plastic pipette with tip cut off6. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted, too! 7. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridge Benthic Microcrustacean Counts Field samplingLeave benthic zooplankton sampler for 24h. Benthic sampler consists of 10 inverted jars with funnel traps in metal grid with 4 feet. Set up on bench using feet (on side) to get a uniform height of the collection jars (lip of jar = 5cm above frame). Upon collection, pull sampler STRAIGHT up, remove jars, homogenize in bucket and bring back to lab. Move the boat slightly to avoid placing sampler directly over cored sediment.Sample preparation in lab1. Sieve integrated samples through 63 micrometer mesh and record volume of full sample2. Collect in Nalgene bottles and make total volume to 50mL3. Add 8 drops of lugol to fix zooplankton.4. Label bottle with sample date, benthic or pelagic zooplankton, and total volume sieved. Samples can be stored in the fridge until time of countingCounting1. Remove sample from fridge2. Sieve sample with 63 micrometer mesh over lab sink to remove Lugol&rsquo;s solution (which vaporizes under light)3. Suspend sample in water in sieve and flush from the back with squirt bottle into counting tray4. Homogenize sample with forceps or plastic pipette with tip cut off5. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted, too!6. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridgeSubsamplingIf homogenized original sample contains more than 500 individuals in the first line of counting tray, you may subsample under the following procedure.1. Return original sample to Nalgene bottle and add water to 50mL2. Homogenize sample by swirling Nalgene bottle3. Collect 10mL of zooplankton sample with Hensen-Stempel pipette4. Empty contents of Hensen-Stempel pipette into large Bogorov tray5. Homogenize sample in tray with forceps or plastic pipette with tip cut off6. Identify (see zooplankton identification guide) using backlit microscope and count with multiple-tally counter. i. Set magnification so that you can see both top and bottom walls of the tray. ii. Change focus depth to check for floating zooplankton that must be counted, too! 7. Pipette sample back into Nalgene bottle, add water to 50mL, add 8 drops Lugol&rsquo;s solution, and return to fridge Chironomid Counts (2012, 2013) For first instar chironomids in top 1.5cm of sediment only (5 samples)1. Use sink hose to sieve sediment through 63 micrometer mesh. You may use moderate pressure to break up tubes.2. Back flush sieve contents into small deli container.3. Return label to deli cup (sticking to underside of lid works well).For later instar chironomids in the section 1.5-11.5cm (5 samples)4. Sieve with 125 micrometer mesh in the field.5. Sieve through 125micrometer mesh again in lab to reduce volume of sample.6. Transfer sample to deli container or pitfall counting tray.For all chironomid samples7. Under dissecting scope, pick through sieved contents for midge larvae. You may have to open tubes with forceps in order to check for larvae inside.8. Remove larvae with forceps while counting, and place into a vial containing 70 percent ethanol. Larvae will eventually be sorted into taxonomic groups (see key). You may sort them into taxonomic groups as you pick the larvae, or you can identify the larvae while measuring head capsules if chironomid densities are low (under 50 individuals per taxanomic group).9. For a random sample of up to 50 individuals of each taxonomic group, measure head capsule, see Chironomid size (head capsule width).10. Archive samples from each sampling date together in a single 20mL glass vial with screw cap in 70 percent ethanol and label with sample contents , Chir, sample date, lake ID, station ID, and number of cores. Chironomid Cound (2014) In 2014, the method for sampling chironomid larvae changed starting with the sample on 2014-06-27; the variable &quot;top_bottom&quot; is coded as a 2. In contrast to previous measurements, the top and bottom core samples were combined and then subsampled. Below is the pertinent section of the protocols.Chironomid samples should be counted within 24 hours of collection. This ensures that larvae are as active and easily identified as possible, and also prevents predatory chironomids from consuming other larvae. Samples should be refrigerated upon returning from the field.<strong>For first instar chironomids in top 1.5cm of sediment only (5 samples)</strong>1. Use sink hose to sieve sediment through 63&micro;m mesh. You may use moderate pressure to break up tubes.2. Back flush sieve contents using a water bottle into small deli container.3. Return label to deli cup (sticking to underside of lid works well).<strong>For larger instar chironomids in the section 1.5-11.5cm (5 samples)</strong>4. Sieve with 125&micro;m mesh in the field.5. Sieve through 125&micro;m mesh again in lab to reduce volume of sample and break up tubes.6. Transfer sample to deli container with the appropriate label.<strong>Subsample if necessary</strong>If necessary, subsample with the following protocol.a. Combine top and bottom samples from each core (1-5) in midge sample splitter.b. Homogenize sample thoroughly, collect one half in deli container, and label container with core number and &ldquo;1/2&rdquo;c. If necessary, split the half that remains in the sampler into quarters, and collect each in deli containers labeled with core number, &ldquo;1/4&rdquo;, and replicate 1 or 2d. Store all deli containers in fridge until counted, and save until all counting is complete&quot; Chironomid Size (head capsule width) 1. Obtain picked samples preserved in ethanol and empty onto petri dish.2. Sort larvae by family groups, arranging in same orientation for easy measurment.3. Set magnification to 20, diopter, x 50 times4. Take measurments for up to 50 or more individuals of each taxa. Round to nearest optical micrometer unit.5. Fill out data sheet for number of larvae in each taxa, Chironomid measurements for each taxa, date of sample, station sample was taken from, which core the sample came from, who picked the core, and your name as the measurer.6. Enter data into shared sheetSee &quot;Chironomid Counts&quot; for changes in sampling chironomid larvae in 2014.
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