Joshua P. DarlingDiane M. McKnight 2024-03-05 tabular digitial data McMurdo Dry Valleys LTER McMurdo Dry Valleys LTER 10.6073/pasta/cc5d88b57b43972807dcb2f6a283388c https://mcm.lternet.edu/content/haptorian-ciliate-measurements-discrete-water-column-samples-using-flow-imaging-microscopy This data package consists of particle diameter and biovolume data for haptorian ciliates classified from discrete water column samples collected at various depths in Lake Fryxell and Lake Hoare in the McMurdo Dry Valleys region of Antarctica. Samples were collected and preserved between November 2007 and January 2020 in Lake Fryxell and between November 2007 and March 2008 in Lake Hoare.  Samples were collected and analyzed as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) core limnological sampling. Data were imaged using flow cytometry (FlowCam VS-IV) and imaged particles were classified with statistical image-based software (Visual Spreadsheet, v4.17.14). Diameter and biovolume was generated for each particle using FlowCam’s area-based diameter (ABD) algorithm. Funding for this work was provided by several awards from the National Science Foundation for Long Term Ecological Research, most recently #OPP-2224760. Additional NSF funding for this work was provided to John Priscu under awards #OPP-0631494 and #MCB-0237335. 2007-11-11 2020-01-02 ground condition As needed The Lake Fryxell basin is formed by a moraine depression in a wider portion of the Taylor Valley. It has a number of moraine islands and shallower areas, as well as several relatively well developed deltas. The lake is fed by at least 10 meltwater streams with a total drainage catchment of 230 km2. The lake is dammed to the southwest by the Canada Glacier and is topographically closed. It is perennially ice covered; during summer months, an ice-free moat generally forms around much of the lake margin. Lake levels have risen ~2 m between 1971 and 1996. There are no surface outflows; the only known water loss is through ice ablation (evaporation, sublimation and physical scouring). Valley: Taylor Distance to Sea : 9 Maximum Length (km): 5.8 Maximum Width (km): 2.1 Maximum Depth (m): 20 Surface Area (km^2): 7.08 Ice Thickness Average Surface (m): 3.3 - 4.5 Volume (m^3 * 10^6): 25.2 163.259582519531 163.048782348633 -77.597076416016 -77.622711181641 18m 18m meter Lake Hoare occupies a narrower portion of the Taylor Valley, dammed by the Canada Glacier. It would drain almost completely without this dam. There are a number of islands which may be related to an old terminal of Canada Glacier. The lake is fed primarily from direct runoff from the glacier, as well as meltwater streams. (Lake level rose ~1.5 m between 1972 and 1996). There are no surface outflows; the only known water loss is through ice ablation (evaporation, sublimation and physical scouring). Valley: Taylor Distance to Sea : 15 Maximum Length (km): 4.2 Maximum Width (km): 1 Maximum Depth (m): 34 Surface Area (km^2): 1.94 Ice Thickness Average Surface (m): 3.1 - 5.5 Volume (m^3 * 10^6): 17.5 162.935836791992 162.784423828125 -77.623085021973 -77.639259338379 73m 73m meter LTER Core Areas population dynamics None <cntperp> <cntper>McMurdo Dry Valleys LTER Information Manager</cntper> </cntperp> <cntemail>im@mcmlter.org</cntemail> Name: Katherina Hell Role: associated researcher Name: Sarah N. Power Role: associated researcher Name: Ryan Hoak Role: associated researcher Name: John C. Priscu Role: associated researcher Name: Renée F. Brown Role: data manager Not Applicable Not Applicable Field and/or Lab Methods Samples were collected and preserved as part of routine McMurdo Dry Valley Long Term Ecological Research (LTER) core limnological sampling. Samples were collected along a 4-16 m depth profile in Lake Fryxell and 4-25 m depth profile in Lake Hoare. Samples preserved in 1 % Lugol’s solution at 5 °C were homogenized on a shaker table (150 RPM for 5 mins) before subsampling 150 mL and settling for 24 hrs. The supernatant was then aspirated off to concentrate the sample by 5-fold. 1-5 mL of homogenized subsample was imaged on FlowCam model VS-IV with a 10X lens and 100 µl flow cell. Images were captured using AutoImage Mode at 18-20 frames sec-1 at a flow rate ranging 0.15-0.20 mL min-1. The fluid volume that was imaged ranged from 5-20 %. Files containing the imaged plankton from each sample were then used to sort and build statistical filters based on particle morphologies. The morphotypes of the imaged plankton were sorted and classified using the Visual Spreadsheet Software (version 4.17.14). From a select number samples that covered a range of times and depths throughout the record, images of like-morphotypes, such as a specific taxon or class of phytoplankton, were manually grouped into taxon- and morph-based libraries. For a total of 27 libraries that covered the range of morphotypes found in the plankton, we built statistical filters based on the specific geometries (e.g., length, width, aspect ratio) of the given particles in each library. The statistical filters were then built into a classification template of seven classes based on broad morphologic characterizations of the plankton.  This classification template, based on statistical filters generated by particle attributes, was then applied to all the samples of interest to automatically sort and classify the image-based data. We chose to assign taxonomic identifiers to only the classes in the classification template with high enough image resolutions for sufficient identification. This condition was met only for the haptorian ciliates of the genera Askenasia and Monodinium because these taxa are known to occur in these lakes and likely not to be confused with other protists. Additional classifications were made for broad algal groups but are not included in this dataset due to poor data quality. Separate classes with filters based on detritus, sediment, and air-bubbles were used to control for unwanted image data. Using our classification template, we were able to automatically classify haptorian ciliates from other microplankton and calculate attribute data (area-based diameter (ABD) and ABD biovolume) for each imaged particle. Samples were collected and preserved as part of routine McMurdo Dry Valley Long Term Ecological Research (LTER) core limnological sampling. Samples were collected along a 4-16 m depth profile in Lake Fryxell and 4-25 m depth profile in Lake Hoare.Samples preserved in 1 % Lugol’s solution at 5 °C were homogenized on a shaker table (150 RPM for 5 mins) before subsampling 150 mL and settling for 24 hrs. The supernatant was then aspirated off to concentrate the sample by 5-fold. 1-5 mL of homogenized subsample was imaged on FlowCam model VS-IV with a 10X lens and 100 µl flow cell. Images were captured using AutoImage Mode at 18-20 frames sec-1 at a flow rate ranging 0.15-0.20 mL min-1. The fluid volume that was imaged ranged from 5-20 %. Files containing the imaged plankton from each sample were then used to sort and build statistical filters based on particle morphologies.The morphotypes of the imaged plankton were sorted and classified using the Visual Spreadsheet Software (version 4.17.14). From a select number samples that covered a range of times and depths throughout the record, images of like-morphotypes, such as a specific taxon or class of phytoplankton, were manually grouped into taxon- and morph-based libraries. For a total of 27 libraries that covered the range of morphotypes found in the plankton, we built statistical filters based on the specific geometries (e.g., length, width, aspect ratio) of the given particles in each library. The statistical filters were then built into a classification template of seven classes based on broad morphologic characterizations of the plankton.  This classification template, based on statistical filters generated by particle attributes, was then applied to all the samples of interest to automatically sort and classify the image-based data.We chose to assign taxonomic identifiers to only the classes in the classification template with high enough image resolutions for sufficient identification. This condition was met only for the haptorian ciliates of the genera Askenasia and Monodinium because these taxa are known to occur in these lakes and likely not to be confused with other protists. Additional classifications were made for broad algal groups but are not included in this dataset due to poor data quality. Separate classes with filters based on detritus, sediment, and air-bubbles were used to control for unwanted image data. Using our classification template, we were able to automatically classify haptorian ciliates from other microplankton and calculate attribute data (area-based diameter (ABD) and ABD biovolume) for each imaged particle. unknown flowcam_haptorid Dataset code Internal dataset code. The data provider Internal dataset code. Limno run code Internal code for core limnological sampling location, date, and run number. The data provider Internal code for core limnological sampling location, date, and run number. Lake name Name of the lake sampled. The data provider Name of the lake sampled. Date sampled. Date sample was collected. The data provider calendar date/time MM/DD/YY h:mm gregorian calendar Depth in meters Depth from surface where the sample was collected in the water column. The data provider meter Diameter in micrometers Particle diameter (width). The data provider micrometer Volume in cubic micrometers Particle biovolume. The data provider micrometer McMurdo Dry Valleys LTER The data distributor shall not be liable for innacuracies in the content http 1 0 1 column , https://mcm.lternet.edu/sites/default/files/data/mcmlter-limno-flowcam_haptorid-20240305.csv None 2024-03-05 2024-03-05 McMurdo Dry Valleys LTER http://mcmlter.org/ Biological Data Profile of the Content Standards for Digital Geospatial Metadata devised by the Federal Geographic Data Committee. Drupal Ecological information Management Systems, version D7, Biological Data Profile module