US Long-Term Ecological Research Network

LAGOS-NE v.1.054.1 - Lake water quality time series and geophysical data from a 17-state region of the United States

Abstract
Time series of mean summer total nitrogen (TN), total phosphorus (TP), stoichiometry (TN:TP) and chlorophyll values from 2913 unique lakes in the Midwest and Northeast United States. Epilimnetic nutrient and chlorophyll observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1, and come from 54 disparate data sources. These data were used to assess long-term monotonic changes in water quality from 1990-2013, and the potential drivers of those trends (Oliver et al., submitted). Summer was used to approximate the stratified period, which was defined as June 15 to September 15. The median number of observations per summer for a given lake was 2, but ranged from 1 to 83. The rules for inclusion in the database were that, for a given water quality parameter, a lake must have an observation in each period of 1990-2000 and 2001-2011. Additionally, observations must span at least 5 years. Each unique lake with nutrient or chlorophyll data also has supporting geophysical data, including climate, atmospheric deposition, land use, hydrology, and topography derived at the lake watershed (variable prefix iws) and HUC 4 (variable prefix hu4) scale. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03. For more specific information on how LAGOS-NE was created, see Soranno et al. (2015).
Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4.
Dataset ID
333
Date Range
-
Methods
See Oliver et al. (submitted) and Soranno et al. (2015) for details on sources of data, methods of collection, and derivation of parameters
Oliver S.K., Collins S.M., Soranno P.A., Wagner T., Stanley E.H., Jones J.R., Stow C.A., Lottig N.R. Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990. Submitted to Global Change Biology.
Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., Gries C., Henry E.N., Skaff N.K., Stanley E.H., Stow C.A., Tan P.-N., Wagner T., and Webster K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4 .
NTL Themes
Version Number
15

LAGOS - Lake nitrogen, phosphorus, and stoichiometry data and geospatial data for lakes in a 17-state region of the U.S.

Abstract
This dataset includes information about total nitrogen (TN) concentrations, total phosphorus (TP) concentrations, TN:TP stoichiometry, and 12 driver variables that might predict nutrient concentrations and ratios. All observed values came from LAGOSLIMNO v. 1.054.1 and LAGOSGEO v. 1.03 (LAke multi-scaled GeOSpatial and temporal database), an integrated database of lake ecosystems (Soranno et al. 2015). LAGOS contains a complete census of lakes great than or equal to 4 ha with corresponding geospatial information for a 17-state region of the U.S., and a subset of the lakes has observational data on morphometry and chemistry. Approximately 54 different sources of data were compiled for this dataset and were mostly generated by government agencies (state, federal, tribal) and universities. Here, we compiled chemistry data from lakes with concurrent observations of TN and TP from the summer stratified season (June 15-September 15) in the most recent 10 years of data included in LAGOSLIMNO v. 1.054.1 (2002-2011). We report the median TN, TP and molar TN:TP values for each lake, which was calculated as the grand median of each yearly median value. We also include data for lake and landscape characteristics that might be important controls on lake nutrients, including: land use (agricultural, pasture, row crop, urban, forest), nitrogen deposition, temperature, precipitation, hydrology (baseflow), maximum depth, and the ratio of lake area to watershed area, which is used to approximate residence time. These data were used to identify drivers of lake nutrient stoichiometry at sub-continental and regional scales (Collins et al, submitted). This research was supported by the NSF Macrosystems Biology program (awards EF-1065786 and EF-1065818) and by the NSF Postdoctoral Research Fellowship in Biology (DBI-1401954).
Dataset ID
332
Methods
See Soranno, P.A., Bissell, E.G., Cheruvelil, K.S., Christel, S.T., Collins, S.M., Fergus, C.E., Filstrup, C.T., Lapierre, J.F., Lottig, N.R., Oliver, S.K., Scott, C.E., Smith, N.J., Stopyak, S., Yuan, S., Bremigan, M.T., Downing, J.A., Gries, C., Henry, E.N., Skaff, N.K., Stanley, E.H., Stow, C.A., Tan, P.-N., Wagner, T., and Webster, K.E. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 4: 28. doi: 10.1186/s13742-015-0067-4 for details on how the observed values were obtained.
See Collins et al. Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales, Submitted to Ecological Applications, for details on data filtering and relationships between nutrient chemistry and landscape characteristics
Version Number
12

Saint Louis River Estuary Water Chemistry, Wisconsin, Minnesota, USA 2012 - 2013

Abstract
These data pertain to water and sediments collected from the Saint Louis River Estuary (SLRE) and its nearby water sources by Luke Loken and collaborators for his Masters thesis and additional publications. In brief, we sampled SLRE surface waters and sediments for a variety of physical, chemical, and biological attributes. Ten estuary stations were sampled approximately monthly from April 2012 through September 2013. On four of the sampling campaigns, water was collected from an additional 20 sites. Sites were selected to represent a gradient from the Saint Louis River to Lake Superior and included several tributaries that drain directly into the estuary. This design aimed to understand the spatial and temporal mixing pattern of the estuary as it receives water from several rivers, 2 waste water treatment plant, and Lake Superior. We sampled the estuary to assess the magnitude and timing of source water contributions to the estuary and establish a baseline of chemical and physical measurements to aid in future limnological research. Additionally, we performed nitrogen and carbon cycling rate experiments to determine the estuary-wide influence on nitrate, ammonium, and dissolved organic carbon. This included 8 sediment denitrification, 1 nitrification, and 2 breakdown dissolved organic carbon (BDOD) surveys. This work was funded by the Minnesota and Wisconsin Sea Grant and in coordination with the establishment of the Lake Superior National Estuary Research Reserve (LSNERR).
Contact
Dataset ID
322
Date Range
-
DOI
10.6073/pasta/08fdc0fb8528e37dd7ef6d6ad2b77f99
Maintenance
completed
Metadata Provider
Methods
We collected water samples from 10 estuary stations to represent a gradient from river to lake on 13 dates between April 2012 and September 2013. Stations 1-5 represented upper estuary sites, while stations 6-10 were lower. Stations were situated near the thalweg, but were shifted laterally to avoid traffic within the shipping channel. Sampling occurred approximately monthly during the open water season when sites were accessed by boat, and once during winter ice cover when a subset of sites were visited on foot. In addition to the core 10 stations, we sampled an additional 20 sites, four times over the two-year study during a high flow and baseflow period. These sites include 7 end members (Saint Louis River, Nemadji River, Bluff Creek, Kinsbury Creek, Pokegama River, and Lake Superior) and an additional 15 in-estuary sites (i.e., stations 16-30). Additional sites were occasionally visited and geographic locations to all stations are provided in SLRESitesTable.Physical LimnologyWe used a YSI EXO2 or 6-Series sonde (Yellow Springs, OH) to measure temperature, specific conductivity, dissolved oxygen, pH, turbidity, and algae fluorescence. Briefly, the sonde was lowered to appr. 0.5 m depth and allowed to stabilize. The sonde was calibrated in the lab that morning according to Lake Superior National Estuary Research Reserve (LSNERR) protocols.Light extinction was determined by lowering a photosynthetically active radiation (PAR) sensor (Licor model 192 or 193) attached to a light meter (Licor model 250A) through the water column. The sensor was allowed to stabilize at 0.25 m depth intervals. We linearly regressed the natural log of the measured light intensity against depth. The slope of this regression is the negative light extinction coefficient (k). Briefly k values closer to zero indicate clearer waters that transit more light.Water ChemistrySurface water from each station was collected into an HDPE carboy and processed in the lab within 10 h of collection. We processed samples in the lab (instead of on the boat) to expedite sample collection so that all stations could be visited within a single day (or within 2 days for spatial intensive surveys). Integrated water samples were taken from 0-2 m using a peristaltic pump or an integrated water sampler and stored in a cooler to maintain ambient temperature. Samples for dissolved solute analysis were filtered through a 0.45 microm Geotech capsule filter. All samples were refrigerated, frozen, or acidified (dependent on the analysis in question) within 12 h of collection. See meta data for SLREWaterChemTable for specifics regarding lab responsible for analyses.Samples for major cations (Calcium (Ca), Iron (Fe), Potassium (K), Sodium (Na), Magnesium (Mg), and Manganese (Mn)) were filtered upon collection into 60 mL acid-washed HDPE bottles, acidified to 1 percent ultrapure hydrochloric acid (HCl) and stored at room temperature until analysis (within 6 months). Cations were analyzed simultaneously on an optical inductively-coupled plasma emission on a Perkin-Elmer model 4300 DV ICP spectrophotometer according to methods outlined at the North Temperate Lakes- Long Term Ecological Research site.Samples for major anions (Chloride (Cl) and sulfate (SO4)) were filtered into a new 20 mL HDPE scintillation vials and stored at 4degree C until analysis (within 3 months). Anion samples were analyzed simultaneously by Ion Chromatography, using a hydroxide eluent determined by a Dionex model ICS 2100 using an electro-chemical suppressor.Samples for dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were analyzed on a Shimadzu TOC analyzer. DOC and DIC samples were filtered into acid-washed 24 mL glass vials and capped with septa, leaving no headspace. DOC samples were acidified with 100 microL of 2 M HCL upon collection. Both DOC and DIC were stored at 4 degreeC, and then analyzed within three weeks at the University of Minnesota-Twin Cities. Both DOC and DIC were collected in duplicate and reported as means.Samples for UV absorbance were filtered into ashed 40 mL glass amber vials and stored at 4degree C until analysis (within 2 months). We measured UV absorbance at 254 nm (Abs254) using a spectrophotometer (Cary 50 UV-Vis Spectrophotometer, Varian, Palo Alto, CA). Specific UV absorbance at 254 nm (SUVA254) was then calculated by dividing Abs254 by the DOC concentration of the water sample.Nitrate plus nitrite nitrogen (referred to as NO3-N), ammonium plus ammonia nitrogen (referred to as NH4-N), and soluble reactive phosphorus (SRP) were analyzed colormetrically. Samples were filtered into new 20 mL plastic scintillation vials and frozen within 8 h of collection. Samples were thawed within 4 months and were analyzed in parallel by automated colorimetric spectrophotometry, using an Astoria-Pacific Astoria II segmented flow autoanalyzer. NO3-N was determined using the automated cadmium reduction method with absorption monitored at lambda=520 nm. NH4-N was determined using the Berthelot Reaction, producing a blue colored indophenol compound, where the absorption was monitored at lambda=660 nm. SRP was determined by forming a phosphoantimonymoledbeun complex and was measured as lambda=880nm.Samples for total and dissolved nitrogen and phosphorus analysis were collected together and in-line filtered (dissolved nitrogen and phosphorus only) into 60 ml LDPE bottles and acidified to a 1 percent HCl. Once acidified, the samples were stored at room temperature until analysis, which occurred within one year. The samples were first prepared for analysis by adding a NaOH–Persulfate digestion reagent and heated for 1 h at 120 degreeC and 18-20 pounds per square inch (psi) in an autoclave. The samples were analyzed for total nitrogen and total phosphorus simultaneously by automated colorimetric spectrophotometry, using a segmented flow autoanalyzer. Total nitrogen is determined by utilizing the automated cadmium reduction method where the absorption is monitored at 520 nm; total phosphorus is determined using ascorbic acid-molybdate method where the absorption is monitored at 880 nm. Both are described in LTER standard methods.We determined dual isotopic natural abundance of nitrate (NO3) and water (H2O) from a subset of collected water samples. Samples for delta18O-NO3 and delta15N-NO3 were filtered into acid-washed 60 mL HDPE bottles and frozen within 8 h of collection. Nitrate isotope samples were analyzed using the denitrifier method at the Colorado Plateau Stable Isotope Laboratory. delta18O-NO3 and delta15N-NO3 isotopes were reported as the per mil (per-mille) deviation from Vienna Standard Mean Ocean Water (VSMOW) and air standards, respectively. Samples for isotopes of water (delta18O-H2O and delta2H-H2O) were collected without headspace in glass vials and measured using isotope ratio infrared spectroscopy at the University of Minnesota – Biometeorology lab. Six replicates were run per sample, and delta18O-H2O and delta2H-H2O were determined relative to VSMOW.Chlorophyll ALaboratory analysis of chlorophyll A (ChlA) uses the Turner Designs model 10-AU fluorometer, following improvements described in Welschmeyer (1994). In this method, ChlA in 90percent acetone is separated from other pigments by the use of specialized optical filters. ChlA samples were preserved within 24 h of water sampling, by collecting filtrand on a 0.2 microm cellulose nitrate filter, placing the filter in a 15 mL falcon tube, and freezing it. Between 200 and 1000 mL of sample was based through the filter until the filter was moderately stained and filtering speed slowed. Within three weeks of collection, filters were thawed, and 12.0 mL of acetone was added to tube, which was allowed to steep for 18-24 h in the dark at 4 degreeC. After steeping, samples were centrifuged at high speed in Sorvall GLC-2B centrifuge for 20 min and warmed to room temperature. Sample fluorescence was then measured on a calibrated Turner Designs model 10-AU fluorometer (excitation 436 nm, emission 680 nm). Sample fluorescence was then converted to a water column concentration by multiplying by the extract volume (i.e., 12 mL) and divided by the volume of water that passed through the filter (i.e., 200-1000 mL).ParticulatesSimilar to ChlA, particulate carbon, nitrogen, and phosphorus samples were collected by passing 200-1000 mL of water through a pre-combusted 0.7 glass fiber filter (GFF) and analyzing the filtrand. Filters were frozen immediately after filtration, and then dried at 60 degreeC for at least 48 hours. Particulate carbon and nitrogen was measured using a Thermo Fisher Flash 2000 elemental analyzer. Particulate phosphorus was determined from a separate filter. Filters were digested in 5 mL potassium persulfate and phosphorus was analyzed spectrophotometrically using the ascorbic acid-molybdate method (Menzel and Corwin 1965).NitrificationWater column nitrification rates were determined on 30 July 2013 for a subset of the water chemistry sampling stations (n = 15) that represented the full spatial extent and previously observed NH4-N range of the estuary. Water from each station was transferred to 333 mL polycarbonate bottles within 10 h of collection and spiked with 15NH4Cl to achieve a concentration of 0.03 micromol 15NH4 L-1. Samples were incubated at ambient temperature (20 degreeC ) in a dark cooler for 20 h. Pre- and post-incubation samples were filtered through 0.45 microm filters and analyzed for NO3-N, NH4-N and delta15N-NO3. Nitrification rates were determined based on changes in NO3-N, NH4-N, and delta15N-NO3 according to methods outlined in Small et al. (2013). Analysis for each station was performed in duplicate and reported as the mean.SedimentsSediments were collected on 8 of the water chemistry survey dates from stations 2-9 to determine spatial and temporal patterns of denitrification and sediment organic content. We also collected a single sediment sample from additional lower (n = 17) and upper (n = 6) stations on 19 June 2012 and 24 June 2013, respectively, to increase the spatial extent of our survey. In total, 56 and 42 individual sediment collections were made in 2012 and 2013, respectively. Sediments were collected from the upper 5-20 cm of the benthic zone using an Ekman dredge. At least 500 mL of benthic material was transferred to 1-L widemouth Nalgene containers and used in denitrification rate experiments. Fifteen mL of the uppermost sediment layer was transferred into sterile 100 mL disposable plastic screw-top containers to be analyzed for sediment composition content. Sediments were stored in a cooler while on the boat and transferred to 4 degreeC within 6 h.To assess the effects of sediment composition on denitrification, dry:wet ratios, bulk density, particle size distributions, loss-on-ignition (LOI), percent carbon, and percent nitrogen were determined from the 15 mL sediment subsamples. Sediments were weighed before and after drying at 60 degreeC for at least 48 h to determine dry:wet ratios and bulk density. Sediment particle size composition was determined optically using a Coulter LS-10 particle size analyzer and sizes were binned into percent clay (0-2.0 microm), silt (2.0-63.0 microm) and sand (63-2000 microm) (Scheldrick and Wang 1993). LOI was determined by the loss in mass of 2.0plus/-0.2 g dried homogenized sediment combusted at 550 degree Celsius for 4 h. Sediments were ground and analyzed for percent carbon and nitrogen using a Thermo Fisher Flash 2000 elemental analyzer.Sediment denitrificationWe determined actual (DeN) and potential (DEA) sediment denitrification rates in the laboratory using the acetylene block technique modified from Groffman et al. (1999) within 48 h of collection. We incubated 40±2 g of wet sediment saturated with 40±0.1 mL of estuary water in 125 mL glass Wheaton bottles at 20 degreeC. DEA incubations were spiked with glucose and NO3-N to a final concentration of 40 mg C L-1 and 100 mg N L-1, respectively; DeN incubations were given no amendments. All incubations were augmented with 10 mg L-1 chloramphenicol to inhibit microbial proliferation (Smith and Tiedje 1979). Samples were capped with rubber septa, flushed with helium (He) for 5 min to remove oxygen (O2), and injected with 10 mL acetylene. We allowed the acetylene 30 min to fully diffuse into the sediment slurry before taking the initial headspace sample (T0). Samples were placed on a shaker table in the dark for 2.6 h then sampled the final headspace (T1). The change in headspace N2O partial pressures (pN2Ofinal - pN2Oinitial) was used to determine the denitrification rate using the Bunsen correction and the ideal gas law. For both T0 and T1 samples, 10 mL of headspace was withdrawn from incubation bottles and injected into a He-flushed 12 mL gas-tight glass vials (Exetainers) sealed with rubber septa. We determined pN2O and pO2 in parallel on a gas chromatograph equipped with an electron capture detector (ECD) and thermal conductivity detector (TCD) using methods outlined in Spokas et al. (2005). Gas samples with O2 concentrations greater than 5percent were removed from analysis due to potential gas leakage. Denitrification rates were standardized to sediment dry mass. Samples collected on or before 6 June 2013 were incubated in triplicate; samples collected after were incubated in duplicate.Denitrification controls were further investigated by amending sediments with combinations of NO3-N and two types of organic carbon: glucose and natural organic matter (NOM; supplied by the International Humic Substance Society). On two dates in 2013, we incubated sediments from five of our core stations that spanned a gradient of sediment organic content with the following amendments: NO3-N only, NO3-N and glucose (DEA), NO3-N and NOM, glucose only, NOM only, and no amendments (DeN). The two carbon treatments were intended to test for possible effects of carbon quality, with NOM representing a recalcitrant, humic-rich carbon source similar to allochthonous materials in the SLRE to contrast the labile glucose treatment. Both carbon sources were amended to 40 mg C L-1, and NO3-N was amended to 100 mg N L-1. Sediments were incubated in parallel (see above).Breakdown Dissolved Organic Carbon (bDOC)Breakdown of DOC (bDOC) was determined from core stations (1-10) from water collected on 23 April and 30 July 2012. Briefly, 250 mL of estuary water was filtered through a 0.45 microM Geotech flow-through filter using a peristaltic pump into sealable glass jars. 25 mL of 2.0 microm filtered water from a common estuary source was added to the filtered jars. DOC samples were collected after 0, 1, 2, 4 ,8, 16, and 32 d and analyzed for DOC (see above). A linear model was fit between time since inoculation and DOC concentration to determine the breakdown of DOC from water column microbes.ReferencesMeyers PA, Teranes JL. 2001. Sediment organic matter. Pages 239-269, In: Track Enviornmental Change Using Lake Sediments Vol 2 Phys Geochemical Methods. Dordrecht: Kluwer Academic Publishers.Groffman, Peter M, Holland EA, Myrold DD, Robertson GP, Zou X. 1999. Denitirification. Pages 272-288 in Standand Soil Methods Long-Term Ecological Research, Oxford University, New York.Menzel DW, Corwin N. 1965. The measurement of total phosphorus in seawater based on the liberation of organically bound fractions by persulfate oxidation. Limnol and Oceanogr 10: 280–282.Scheldrick HB, Wang C. 1993. Particle size distribution. Pages 499-512 In: Soil Sampling and Methods of Analysis. Boca Raton: CRC Press LLC.Small GE, Bullerjahn GS, Sterner RW, Beall BFN, Brovold S, Finlay JC, McKay RML, Mukherjee M. 2013. Rates and controls of nitrification in a large oligotrophic lake. Limnol Oceanogr. 58:276–86.Smith MS, Tiedje JM (1979) Phases of denitrification following oxygen depletion in soil. Soil Biol Biochem 11:261-267Spokas K, Wang D, Venterea R. 2005. Greenhouse gas production and emission from a forest nursery soil following fumigation with chloropicrin and methyl isothiocyanate. Soil Biol Biochem. 37:475–85.Welschmeyer, N.A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol Oceanogr 39:1985-1992. 
Version Number
17

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.
Version Number
17

LTREB Chemical and Physical Limnology at Lake Myvatn 2012-current

Abstract
These data are part of a long-term monitoring program at station 33 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, Tanytarsus gracilentus.
Creator
Dataset ID
287
Date Range
-
Maintenance
Ongoing
Metadata Provider
Methods
Water Profile1. Take Light, DO, pH, Temp profile every 0.5mUse YSI DO probe, pH meter, and Li Cor light meter. Take the light profile from the sunny side of the boat.2. Take Secchi depthLower Secchi disk slowly until you can never see clear boundaries between white and black quarters, record this distance to the surface of the water as lower Secchi disk observation. Then pull the Secchi up until you can always see clear boundaries between white and black quarters, record this distance to the surface as the upper Secchi observation.Benthic Net Primary Production1. Measure light, temperature, percentDO, DO, and pH at 0.5m intervals at the sampling location.2. Take 10 clean/undisturbed cores. Try to get a uniform distance between the sediment and top of tube, so the cores have the same volume of water. Cover in boat with tarp to exclude light.3. Collect water from the shore of the boat and measure temp, percentDO, and DO. Save in bucket.4. Measure light intensity at 0 (out) and 0.5m depth where the cores will be incubated.5. Set up HOBO light recorder on the incubator.6. For each tube, take initial temp, percentDO, and DO. Before taking DO measurement, move the DO probe up and down three times to ensure no DO gradient (but do not disturb sediment). Add, slowly and without bubbling, 10 to 20mL of water (just the amount needed) to the core from bucket (number 3) to ensure no air space, and replace the stopper. Measure the distance from sediment to bottom of stopper to the nearest 0.5cm (column_depth).7. Place cores 1, 3, 5, and 7 in dark chambers (opaque tubes), so there are 4 dark and 6 light treatments.8. Incubate the cores using the metal structure at saturation light intensity if possible (300 mol per meter squared per second at 0.5m depth) for about 3h.9. Before taking DO measurement, move the DO probe up and down three times to ensure no DO gradient (but do not disturb sediment), and then measure percentDO, DO, and temperature in each core.Light controlsOnce a month (June, July, August), on a sunny day, incubate 10 cores for 3h with different light intensities to determine primary productivity under different light intensities and different temperatures. It would be best to do this the day after routine sampling (i.e., when retrieving the benthic sampler) so that the results can be compared to those from the routine sampling. Different light levels are obtained using white mesh bags around the core tubes.Core 1 and 6, lightCore 2 and 7, 2xCore 3 and 8, 4xCore 4 and 9, 8xCore 5 and 10, darkIMPORTANT: After the incubations, measure light intensity inside a core tube covered for the different treatments. This is done by removing the light meter from the metal holder and placing it facing up in a core using zip ties and a blue stopper at the bottom. Then place treatment bags over the top and measure light when holding the core at the level they reach in the incubator; use the marking on the light meter cord to make sure this is standardized for all measurements. This should be done 8 times total (each bag plus twice without bags).Light saturationOnce a month in the summer of 2013, we conducted sediment core incubations with varying amounts of shade cloth applied to the cores. Sediment cores received 0, 2, 4, 8, or 15 layers of shade cloth, with two cores in each treatment. All cores were then incubated in the lake over the same 3hr period at a depth of 0.5m.Sediment Dry Weight and Weight on Combustion1. Remove 0.75cm of sediment from a core into a plastic deli container. This should be done on a fresh core. This is the same sample that is used for chl analysis.2. Subsample 5 to 10mL sediment solution and place in a pre-weighed tin tray in oven at 60C for at least 12 hours. When dry, weigh for dry weight.In 2014, the method for sampling benthic chlorophyll changed. Sediment Dry Weight measurements were taken from these samples as well. 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 (15.3 mm diameter). Take 4-5cm of sediment. Then, remove bottom 2cm and place top 2cm in the film canister.3. Combust at 550C for 4.5 hours. Weigh tray.4. If not analyzing combusted samples immediately, place in drying oven before weighing.
Version Number
15

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

Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010

Abstract
Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, "Modeling Ice Cover and Water Temperature of Lake Mendota."; Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations. From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157.
Dataset ID
283
Date Range
-
Maintenance
ongoing
Metadata Provider
Methods
Ice and snow sampling was conducted weekly from 14 January to 30 March, 2010 on Lake Mendota when the ice was safe to walk on. A Kovacs Mark III core drill, manufactured by Ice Coring and Drilling Service (ICDS), Space Science and Engineering Center (SSEC) UW Madison, was used to collect ice cores. Snow depth was also measured at the locations where ice cores were sampled. All measurements were made in centimeters. Blue ice can be defined as the portion of the ice core that is strictly frozen lake water. White ice can be defined as &ldquo;snow ice,&rdquo; which occurs when water rushes through cracks in the ice and soaks the overlying snow, resulting in a mixture of ice and snow that subsequently freezes. Total ice is blue ice + snow ice. Finally, snow depth was calculated as the average of 10 snow depth samples at each sampling location.
Version Number
19
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