For data content questions, contact Niwot LTER data manager
For methodology questions, contact Diane McKnight
INSTAAR, University of Colorado
1560 30th St., UCB 450
Boulder, CO 80309-0450
Search tips: Core Area, Discipline, Site, Variable, Investigator, Year, etc.
1. Use our data freely. All Niwot LTER data products (except some recent data sets for which metadata but not data are available) are released to the public and may be freely copied, distributed, edited, remixed, and built upon under the condition that you provide acknowledgement as described below.
2. Give proper acknowledgement. Publications, models and data products that make use of these data sets must include proper acknowledgement, including citing data sets in a similar way to citing a journal article. See http://www.datacite.org/whycitedata). The following acknowledgment should accompany any publication or citation of these data: Logistical support and/or data were provided by the NSF supported Niwot Ridge Long-Term Ecological Research project and the University of Colorado Mountain Research Station
3. Let us know how you will use the data. The data set creators and Niwot LTER would appreciate hearing of any plans to use the data set. Consider consultation or collaboration with data set creators.
4. Although efforts have been made to ensure that these data are of the highest quality, the possibility of errors exists. Plase notify the data manager of any errors at firstname.lastname@example.org.
These data represent phytoplankton community composition in Green Lake 4 observed during bi-weekly sampling events following ice-off starting with the 2008 field season. Samples were collected from the deepest part of the lake using a Van Dorn sampler from the surface, as well as depths of 3 and 9 m. Samples from the inlet and outlet are grab samples. Phytoplankton samples were preserved in 1% Lugol’s solution. Cell counts and biovolumes for each taxonomic group were measured using a FlowCAM. Taxonomic resolution was based on the imaging capabilities of the instrument and varies based on the size and distinguishing characteristics of the cells in a given taxonomic group. Please see details in Methods.
Green Lake 4: latitude 40.05545; longitude -105.62091; elevation 3584 m.
2008-7-29 to ongoing
depth, phylum, genus, cell density, biovolume
Green Lake 4
Elevation: 3550 m
Green Lakes Valley (City of Boulder Watershed)
Located in: City of Boulder Watershed
(Click to learn more about these locations)
More data from: Green Lake 4
Core Data Set: yes Core Area: Populations
Discipline: Limnology, Plant/vegetation ecology, Microbial ecology
Award Or Grant: DEB1637686
Sampling: Starting immediately after the ice has completely melted from the lake, samples are taken once a week for six consecutive weeks from the inlet, the outlet and at the deepest portion of the lake at 0, 3, and 9 m using an inflatable raft. Samples from the inlet, outlet and the surface are grab samples and the samples from deeper depths in the lake are taken using a 2L Van Dorn sampler. Phytoplankton samples are collected in clean 500mL amber Nalgene bottles. The bottles are rinsed with the respective sample water three times before filling the bottle with actual sample water. Bottles are labeled with date (yyyy/mm/dd), time hh:mm (MST), lake and location (inlet, outlet, depth) and kept dark and cool until returning to the lab. Upon returning to the lab, phytoplankton samples are preserved with 1% Lugol's solution and stored at room temperature until analysis.
Sample preparation: For analysis with the FlowCAM the sample is first put on a shaking table at 150 rpm for 4 minutes to ensure a well mixed sample. Then, 150 mL of the mixed sample is poured into large conical centrifuge bottles which are labeled with all the sample information. The sample is settled in these centrifuge tubes for 24h in an upright position and they are not disturbed during that time. After 24h, 150 mL are aspirated to 30mL. From these 30 mL, three 5mL replicates are then run on the FlowCAM.
Analysis: All taxa included in this data set were imaged using a Benchtop FlowCAM VS Model # VS-IV-C B3. Before a sample is run, the FlowCAM is cleaned thoroughly three times with ethanol followed by one time with water. When running the water through, the run is imaged to make sure no particles are stuck in the FlowCell to prevent any contamination. If particles are still visible during the water run the whole cleaning procedure is repeated until no more particles are visible. After cleaning, the FlowCAM is focused using a solution that represents the size distribution of the sample. During the focusing the camera gain should be set so that an intensity mean of about 180 is reached to ensure best possible light intensity. Before samples are run, the flow rate and the frames per second are adjusted to attain the highest possible efficiency (with a non field of view FlowCell, an efficiency of about 25% is the highest possible). The top and bottom tube lengths of the FlowCell must be checked and entered correctly. After focusing and the other adjustments, three 5 mL replicate subsamples of the sample can be run through a 100μm * 2mm FlowCell using a 10x obejective. The runs are saved in the following format (mm_dd_yyyy_lake_depth_Run). Between different replicates and samples, the FlowCell is cleaned in the manner described for the initial cleaning.
Sample classification: Libraries with most of the present phyla and genera are established for each lake. Based on the libraries, filters can then be made that will help to sort through individual samples and during automatic classification. Once libraries and filters are made, each sample can be run through an automatic classification which sorts through all the pictures taken during a sample run. The pictures are automatically sorted into the different classes, which are defined by the filters established earlier. After the automatic classification, the person analyzing the samples will have to go through each sample run and make sure that particles have been put into the correct respective class. If foreign particles are found, they should be moved to the correct class. If particles are pollen, debris, zooplankton etc. and are not part of the phytoplankton, they should be put into their own category/class named unknown (or similar). After classified samples have been checked for classification errors by the FlowCAM, the classification summary data (abundance [paricle/mL], mean particle biovolume for each taxonomic group, etc.) can be exported into an Excel spreadsheet. These data are calculated by the FlowCAM and adjusted for the established run efficiency and the actual sample volume imaged. Representative images of each taxonomic group can be found at the Rocky Mountain LAKE ALGAE online database at http://niwotlter.colorado.edu/lake-algae/taxa/index.php
Data: Data are entered based on the FlowCAM classification summary data. If available, data from all three replicate runs (identified as Run1-Run3 in the sample ID) are reported, including particle density [particles/mL] and mean particle biovolume for each taxonomic group in each sample. Particle densities reported by the FlowCAM do not reflect settling, thus the particle densities in this data set have been corrected as follows:
C1 = particle density of specific genus in original sample
C2 = particle density of specific genus in settled sample (recorded by FlowCAM as particles/mL)
V1 = original volume (150mL)
V2 = volume of settled sample (30mL).
The biovolume is automatically calculated by the FlowCAM based on the particle diameter. Biovolume (um3/mL) for each taxonomic group in each sample is estimated as particle density (particles/mL) * mean particle biovolume (um3/mL).
Small particles are often indistiguishable on the FlowCAM and thus can't be accurately be identified. Furthermore, the fixation with Lugol's can cause a decrease of abundance of nanoplankton (2-20μm equivalent spherical diameter) due to their enhanced concentration into aggregates or their disintegration into smaller particles beyon the detection range of the FlowCAM. While we can't account for small disintegrated particles below the detection range, we tried to find a correction factor for aggregated small particles that are indistinguishable for identification. The FlowCAM counts multiple particles in one big aggregate as one big particle. We counted 100 images of such aggregates of six samples from each depth and location for each season between 2008 and 2014. We found that there are on average 7.49 particles per aggregate with a standard deviation of +/- 5.04.
We put these aggregates and very small indistiguishable particles into a class called LGRT (little green round things). The following phyla and genera are most likely to be represented in aggregates comprising the LGRT class:
Cyanophyta: Cyanophyte sp.
Chlorophyta: Scenedesmus ecornis, Chlamydomonas sp. #1 and #2, Chlorella minutissima, Chlorella sp., Sphaerocystis sp.
Crysophyta: Chromulina sp.
Reference (in addition to Citations):
Zarauz, L., and X. Irigoien. 2008. Effects of Lugol’s fixation on the size structure of natural nano–microplankton samples, analyzed by means of an automatic counting method. Journal of Plankton Research 30:1297-1303.
Refer to http://niwotlter.colorado.edu/lake-algae/ for photomicrographic records and taxa information.
Also, metadata for older phytoplankton data collected with a different method (inverted microscope) are available at:
phytoplankton, Green Lake 4, community composition, abundance, biovolume, richness, cell density, phylum, genus, Niwot Ridge LTER, NWT, long term
COL1. label=sample_id, type=string, units=none, missing value indicator=NaN, minimum=, maximum=, precision=, definition=sample ID code
COL2. label=date, type=string, units=none, missing value indicator=NaN, minimum=2008-07-29, maximum=, precision=, definition=date (yyyy-mm-dd) sample was collected
COL3. label=year, type=integer, units=none, missing value indicator=NaN, minimum=2008, maximum=, precision=l, definition=year sample was collected
COL4. label=lake, type=string, units=none, missing value indicator=NaN, minimum=GL4, maximum=GL4, precision=, definition=lake (Green Lake 4)
COL5. label=depth, type=string, units=meter missing value indicator=NaN, minimum=, maximum=, precision=, definition=depth in meters at which sample was collected or location (inlet or outlet)
COL6. label=phylum, type=string, units=none, missing value indicator=NaN, minimum=, maximum=, precision=, definition=phylum level taxonomic resolution
COL7. label=genus, type=string, units=none, missing value indicator=NaN, minimum=, maximum=, precision=, definition=lowest practical taxonomic unit (mostly genus)
COL8. label=cell_density, type=real, units=numberPerMilliliter, missing value indicator=NaN, minimum=, maximum=, precision=, definition=number of particles imaged standardized to ml of sample
COL9. label=biovolume, type=real, units=micrometerCubedPerMilliliter, missing value indicator=NaN, minimum=, maximum=, precision=, definition=biovolume of particles imaged standardized to ml sample volume
*Note: To ask a question about data content, please contact the data manager HERE
To ask a question about methodology, please contact Diane McKnight,
 Gardner, E. M., D. M. McKnight, W. M. Lewis, and M. P. Miller. 2008. Effects of Nutrient Enrichment on Phytoplankton in an Alpine Lake, Colorado, U.S.A. Arctic, Antarctic, and Alpine Research 40:55–64.
 Flanagan, C. M., D. M. McKnight, D. Liptzin, M. W. Williams, and M. P. Miller. 2009. Response of the Phytoplankton Community in an Alpine Lake to Drought Conditions: Colorado Rocky Mountain Front Range, U.S.A. Arctic, Antarctic, and Alpine Research 41:191–203.
 Miller, M. P., and D. M. McKnight. 2012. Limnology of the Green Lakes Valley: phytoplankton ecology and dissolved organic matter biogeochemistry at a long-term ecological research site. Plant Ecology & Diversity 0:1–14.
The data set and metadata were provided by Eric Sokol.[HCH 25 January 2016] The methods and data were updated by information provided by Eric Sokol. Corrections were made to taxonomic names and biovolume amounts, and units were added for cell density and biovolume.[HCH 26 April 2017]
McKnight, Diane. Hell, Katherina. Sokol, Eric. 2018. FlowCAM-based phytoplankton community composition for Green Lake 4 from 2008-7-29 to ongoing, biweekly. http://niwot.colorado.edu
This material is based upon work supported by the National Science Foundation under Cooperative Agreement #DEB-1637686. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necesarily reflect the views of the National Science Foundation.
Please contact email@example.com with questions, comments, or for technical assistance regarding this website.