I. Data Set Descriptors A. Title: Hongyu Guo. 2014. Biomass and abiotic variable data in the study of the ecosystem engeneering effect of oysters on Suaeda linearis distribution in Georgia salt marshes (2008-2009). Georgia Coastal Ecosystems LTER Data Catalog (data set PLT-GCET-1405; http://gce-lter.marsci.uga.edu/public/app/dataset_details.asp?accession=PLT-GCET-1405) B. Accession Number: PLT-GCET-1405 C. Description 1. Originator(s): Name: Hongyu Guo Address: Department of Biology and Biochemistry 369 Science and Research Bldg 2 Houston, Texas 77204-5001 Country: USA Email: greatuniverse@hotmail.com 2. Abstract: Oysters are ecosystem engineers in marine ecosystems, but the functions of oyster shell deposits in intertidal salt marshes are not well understood. The annual plant Suaeda linearis is associated with oyster shell deposits in Georgia salt marshes. We hypothesized that oyster shell deposits promoted the distribution of Suaeda linearis by engineering soil conditions unfavorable to dominant salt marsh plants of the region (the shrub Borrichia frutescens, the rush Juncus roemerianus and the grass Spartina alterniflora). We tested this hypothesis using common garden pot experiments and field transplant experiments. Suaeda linearis thrived in Borrichia frutescens stands in the absence of neighbors, but was suppressed by Borrichia frutescens in the with-neighbor treatment, suggesting that Suaeda linearis was excluded from Borrichia frutescens stands by interspecific competition. Suaeda linearis plants all died in Juncus roemerianus and Spartina alterniflora stands, indicating that Suaeda linearis is excluded from these habitats by physical stress (likely water-logging). In contrast, Borrichia frutescens, Juncus roemerianus and Spartina alterniflora all performed poorly in Suaeda linearis stands regardless of neighbor treatments, probably due to physical stresses such as low soil water content and low organic matter content. Thus, oyster shell deposits play an important ecosystem engineering role in influencing salt marsh plant communities by providing a unique niche for Suaeda linearis, which otherwise would be rare or absent in salt marshes in the southeastern US. Since the success of Suaeda linearis is linked to the success of oysters, efforts to protect and restore oyster reefs may also benefit salt marsh plant communities. 3. Study Type: Graduate Thesis Study 4. Study Themes: Plant Ecology, Population Ecology 5. LTER Core Areas: Populations 6. Georeferences: none 7. Submission Date: Mar 14, 2014 D. Keywords: biodiversity, Borrichia frutescens, community structure, competition, distribution, ecosystems, habitat selection, habitats, Juncus roemerianus, oyster shell, Populations, salt marshes, Spartina alterniflora, Suaeda linearis II. Research Origin Descriptors A. Overall Project Description 1. Project Title: Georgia Coastal Ecosystems LTER Project 2. Principal Investigators: Name: Merryl Alber Address: Dept. of Marine Sciences University of Georgia Athens, Georgia 30602-3636 Country: USA Email: malber@uga.edu 3. Funding Period: May 01, 2006 to Jan 01, 2013 4. Objectives: To understand the mechanisms by which variation in the quality, source and amount of both fresh and salt water create temporal and spatial variability in estuarine habitats and processes, in order to predict directional changes that will occur in response to long-term shifts in estuarine salinity patterns 5. Abstract: The Georgia Coastal Ecosystems (GCE) LTER program, located on the central Georgia coast, was established in 2000. The study domain encompasses three adjacent sounds (Altamaha, Doboy, Sapelo) and includes upland (mainland, barrier islands, marsh hammocks), intertidal (fresh, brackish and salt marsh) and submerged (river, estuary, continental shelf) habitats. Patterns and processes in this complex landscape vary spatially within and between sites, and temporally on multiple scales (tidal, diurnal, seasonal, and interannual). Overlain on this spatial and temporal variation are long-term trends caused by climate change, sea level rise, and human alterations of the landscape. These long-term trends are likely to manifest in many ways, including changes in water quality, river discharge, runoff and tidal inundation patterns throughout the estuarine landscape. The overarching goal of the GCE program is to understand the mechanisms by which variation in the quality, source and amount of both fresh and salt water create temporal and spatial variability in estuarine habitats and processes, in order to predict directional changes that will occur in response to long-term shifts in estuarine salinity patterns. The objectives of the current funding cycle are 1) to continue to document long-term patterns of environmental forcing to the coastal zone, 2) to link environmental forcing to observed spatial and temporal patterns of biogeochemical processes, primary production, community dynamics, decomposition and disturbance, 3) to investigate the underlying mechanisms by which environmental gradients along the longitudinal (freshwater-saltwater) and 4) lateral (upland-subtidal) axes of estuaries drive ecosystem change, and 5) to explore the relative importance of larval transport and the conditions of the adult environment in determining community and genetic structure across both the longitudinal and vertical gradients of the estuary. To meet these objectives, we utilize a suite of approaches including long-term monitoring of abiotic drivers and ecosystem responses; manipulative and natural experiments designed to enable us to examine the importance of key ecosystem drivers; and modeling. 6. Funding Source: NSF OCE 0620959 B. Sub-project Description 1. Site Description a. Geographic Location: Coordinates: b. Physiographic Region: c. Landform Components: d. Hydrographic Characteristics: e. Topographic Attributes: f. Geology, Lithology and Soils: g. Vegetation Communities: h. History of Land Use and Disturbance: none recorded i. Climate: Climate summary for Sapelo Island, Georgia, based on NWS data from 1980-2010: Daily-aggregated Values: Mean (sample standard deviation) mean air temperature: 20.09°C (7.28°C) minimum air temperature: 15.02°C (7.96°C) maximum air temperature: 24.82°C (6.98°C) total precipitation: 3.26mm (10.3mm) Yearly-aggregated Daily Values: Mean (sample standard deviation) total precipitation (1980-2010): 1124mm (266mm) 2. Experimental or Sampling Design a. Design Characteristics: Study 1: Common Garden Pot Experiment -- Fieldwork was conducted at salt marshes near Sapelo Island, Georgia, USA (31° 27´N, 81° 16´W). In these salt marshes, Borrichia frutescens and Juncus roemerianus occur in monospecific or mixed stands at higher marsh elevations, and Spartina alterniflora at lower marsh elevations (Wiegert and Freeman 1990; Pennings and Moore 2001). Suaeda linearis occurs in discrete stands at high marsh elevations adjacent to Borrichia frutescens or Juncus roemerianus, and at low marsh elevations adjacent to large tidal creeks and channels (all plants will be referred to generically hereafter). Common Garden Pot Experiment 1 (CGPE I) was carried out between June 2008 and October 2008. Common Garden Pot Experiment 2 (CGPE II) was carried out between June 2009 and October 2009. Study 2: Field Survey -- Fieldwork was conducted at salt marshes near Sapelo Island, Georgia, USA (31° 27´N, 81° 16´W). In these salt marshes, Borrichia frutescens and Juncus roemerianus occur in monospecific or mixed stands at higher marsh elevations, and Spartina alterniflora at lower marsh elevations (Wiegert and Freeman 1990; Pennings and Moore 2001). Suaeda linearis occurs in discrete stands at high marsh elevations adjacent to Borrichia frutescens or Juncus roemerianus, and at low marsh elevations adjacent to large tidal creeks and channels (all plants will be referred to generically hereafter). Study 3: Field Transplant Experiments -- Fieldwork was conducted at salt marshes near Sapelo Island, Georgia, USA (31° 27´N, 81° 16´W). In these salt marshes, Borrichia frutescens and Juncus roemerianus occur in monospecific or mixed stands at higher marsh elevations, and Spartina alterniflora at lower marsh elevations (Wiegert and Freeman 1990; Pennings and Moore 2001). Suaeda linearis occurs in discrete stands at high marsh elevations adjacent to Borrichia frutescens or Juncus roemerianus, and at low marsh elevations adjacent to large tidal creeks and channels (all plants will be referred to generically hereafter). We located 3 representative sites where Suaeda occurred associated with oyster shell deposits adjacent to stands of Borrichia, Juncus or Spartina. Henceforth we refer to these sites as the Borrichia-Suaeda site, Juncus-Suaeda site and Spartina-Suaeda site. The Borrichia-Suaeda site was at the terrestrial border of a high marsh and only experienced occasional tidal flooding during spring tides with relatively short duration; the Juncus-Suaeda site was in the high marsh, and experienced irregular tidal flooding that was most prolonged during spring-tides; the Spartina-Suaeda site was at low marsh elevations, and experienced daily tidal flooding. b. Permanent Plots: Study 1: none Study 2: none Study 3: none c. Data Collection Duration and Frequency: Study 1: CGPE I - We grew Suaeda, Borrichia and Juncus alone (1 plant per pot) and in the combinations of Suaeda + Borrichia and Suaeda + Juncus (1 plant of each species per pot) in 20-L (29 cm wide × 35 cm high) pots. Pots were filled with 3 media treatments: 1) high marsh soil collected from a single high marsh, 2) oyster shell collected from a single tidal channel bank and 3) a mixture of 50% (volume) soil (from high marsh) and 50% (volume) shell (n = 10 replicates per species combination per media treatment). For each species, healthy individual plants (seedlings for Suaeda) within a narrow range of size were collected from a single site in June 2008. The roots of each plant were carefully separated from the original soil and rinsed with fresh water before potting. Pots assigned to without-neighbor treatments were placed apart from each other by ~0.5 m to ensure minimal shading from plants in adjacent pots, whereas pots with same combination of plants in with-neighbor treatments were placed close to each other to simulate an extensive mixed stand as best as possible given a pot experiment. CGPE II: In this experiment, we grew Suaeda and Spartina alone and together with 3 media treatments which were low marsh soil, shell and a mixture of 50% (volume) soil (from the low marsh) and 50% (volume) shell (n = 10 replicates per species combination per media treatment). Both experiments ran from June to October. Plants were maintained outside (exposed to rain) and watered evenly with fresh water (1L per pot) on non-rainy days. The pots received full sun during the middle of the day and partial shade from trees and a building in the early morning and late afternoon. At the end of growing season (October), aboveground biomass of all plants was harvested and dried for 3 days at 60 degrees C and weighed. We measured the volumetric water content of media in each pot at the end of growing season by drying soils (~8 hours after watering) as described above. Study 2: To document the association between Suaeda and oyster shell deposits, we took soil samples from stands of Suaeda at 20 separate locations. For comparison, we also took soil samples respectively from stands of Borrichia (n = 10 locations), Juncus (n = 10 locations) and Spartina (n = 10 locations) that were adjacent to Suaeda stands. Discrete stands of Suaeda were identified from a distance of ~50 m (at this distance we could not see whether soils had a high shell content or not). At each location, we collected soil samples (0-10 cm depth) at 3-5 spots from within a stand, and combined them into a mixed soil sample of ~4L in volume. We measured the wet weight of a subsample (50 ml volume) of each soil sample, and dried the subsamples for 3 days at 60 degrees C, then weighed them again to determine water volume, assuming 1g/ml as the water density. We calculated the volumetric soil water content using the formula: volumetric soil water content = water volume (ml)/total soil volume (ml). We separated the rest of each soil sample into oyster shell versus sand and clay using a sieve (mesh size 5.6 mm). The very few gravel and rock pieces found were included with the sand/clay fraction. We calculated oyster shell content in the soil using the formula: oyster shell content = oyster shell dry mass (g)/soil total dry mass (g). Study 3: In each vegetation zone of each site, we established 8 removal plots (1.5 × 1.5 m) with background vegetation removed, and 8 control plots with background vegetation left intact that were interspersed with the removal plots, for a total of 32 plots per site (2 vegetation zones × 2 neighbor treatments × 8 replicates). The removal plots were created by clipping background vegetation at the soil surface in March 2009, and maintained by monthly weeding. At each site, healthy Suaeda seedlings and individuals of the paired species (Borrichia, Juncus or Spartina), with associated soil blocks (10×10×10 cm), were collected and transplanted into each plot in each vegetation zone. Because some soil was transplanted along with perennial plants, this experiment was conservative with respect to the effect of habitat on the performance of the perennials. For each species, individuals collected were within a narrow size range. The 2 individuals (of Suaeda and the other paired species) in each plot were placed ~1m apart from each other within the plot to minimize any interactions between them (their canopies never overlapped). At the end of the growing season (October 2009), all aboveground live plant material was harvested, dried for 3 days at 60 degrees C and weighed. Soil water content (volumetric), porewater salinity and pH were measured in July, August and September 2009. Soil organic matter content was determined in July 2009 by combustion method. Beginning of Observations: Study 1: Jun 01, 2008 Study 2: Aug 01, 2008 Study 3: Mar 01, 2009 End of Observations: Study 1: Oct 31, 2009 Study 2: Aug 30, 2008 Study 3: Oct 31, 2009 3. Research Methods a. Field and Laboratory Methods: Method 1: Aboveground Biomass of Target Species - Garden Pot -- At the end of growing season (October 2008 for the common garden pot experiment I, and October 2009 for the common garden pot experiment II), all aboveground live plant material was harvested and dried for 3 days at 60 degrees C and weighed. Method 2: Aboveground Biomass of Target Species - Transplant -- At the end of the growing season (October 2009), all aboveground live plant material was harvested, dried for 3 days at 60 degrees C and weighed. Method 3: Oyster Shell Content - Field Survey -- We collected soil samples from stands of Suaeda at 20 separate locations. For comparison, we also took soil samples respectively from stands of Borrichia (n = 10 locations), Juncus (n = 10 locations) and Spartina (n = 10 locations) that were adjacent to Suaeda stands. Discrete stands of Suaeda were identified from a distance of ~50 m (at this distance we could not see whether soils had a high shell content or not). At each location, we collected soil samples (0-10 cm depth) at 3-5 spots from within a stand, and combined them into a mixed soil sample of ~4L in volume. For each soil sample, we took a subsemple of ~3.95L and separated the subsample into oyster shell versus sand and clay using a sieve (mesh size 5.6 mm). The very few gravel and rock pieces found were included with the sand/clay fraction. We calculated oyster shell content in the soil using the formula: oyster shell content = oyster shell dry mass (g)/soil total dry mass (g). Method 4: Soil Organic Matter Content - Field Transplant -- In July 2009, we collected soil samples of ~50g from 0-10 cm depth from 8 spots (8 samples) from each of the 2 vegetation zones at each site (in total 48 samples = 8 samples x 2 zones x 3 sites). We dried the soil samples in the lab for 16h at 110 °C, and grinded the samples into powder. We took a subsample (~15g) from each soil sample and determined the weight of each subsample, and placed the subsamples in a cold muffle furnace and raise the temperature to 400°C. We heated the subsamples for 16 h, and determined the weight of each subsample again. We calculated soil organic content (%) using the formula: soil organic matter content = 1-weight after combustion (g)/ weight before combustion (g). Method 5: Soil pH - Field Transplant -- Soil smaples were collected in July, August and September 2009 respectively. At each time, we collected soil samples of 50 ml in volume from 0-10 cm depth from 8 spots (8 samples) from each of the 2 vegetation zones at each site (in total 144 samples = 8 samples x 2 zones x 3 sites x 3 times). Soil pH was measured by rehydrating dried soils with distilled water in a 1:1 soil:water mixture (by volume), and measuring the pH of the supernatant with a pH meter. Method 6: Soil Porewater Salinity - Field Transplant -- Soil smaples were collected in July, August and September 2009 respectively. At each time, we collected ~50g soil samples from 0-10 cm depth from 8 spots (8 samples) from each of the 2 vegetation zones at each site (in total 144 samples = 8 samples x 2 zones x 3 sites x 3 times). Soil porewater salinity was measured by rehydrating dried soils in a known volume of distilled water, measuring the salinity of the supernatant, and back-calculating to the original porewater volume. Method 7: Volumetric Soil Water Content - Common Garden Pot -- We measured the volumetric water content of media in each pot at the end of growing season (October 2008 for the common garden pot experiment I, and October 2009 for the common garden pot experiment II) at ~8 hours after the last watering. We collected soil sample of 50 ml in volume from 0-10 cm depth for each pot of different media. We measured the wet weight of each soil sample, and dried the soil samples for 3 days at 60 degrees C, then weighed them again to determine water volume, assuming 1g/ml as the water density. We calculated the volumetric soil water content using the formula: volumetric soil water content = water volume (ml)/total soil volume (ml). Method 8: Volumetric Soil Water Content - Field Transplant -- Soil smaples were collected in July, August and September 2009 respectively. At each time, we collected soil samples of 50 ml in volume from 0-10 cm depth from 8 spots (8 samples) from each of the 2 vegetation zones at each site (in total 144 samples = 8 samples x 2 zones x 3 sites x 3 times). We measured the wet weight of each soil sample, and dried the soil samples for 3 days at 60 degrees C, then weighed them again to determine water volume, assuming 1g/ml as the water density. We calculated the volumetric soil water content using the formula: volumetric soil water content = water volume (ml)/total soil volume (ml). Method 9: Volumetric Soil Water Content - Field Survey -- We collected soil samples from stands of Suaeda at 20 separate locations. For comparison, we also took soil samples respectively from stands of Borrichia (n = 10 locations), Juncus (n = 10 locations) and Spartina (n = 10 locations) that were adjacent to Suaeda stands. Discrete stands of Suaeda were identified from a distance of ~50 m (at this distance we could not see whether soils had a high shell content or not). At each location, we collected soil samples (0-10 cm depth) at 3-5 spots from within a stand, and combined them into a mixed soil sample of ~4L in volume.We measured the wet weight of a subsample (50 ml volume) of each soil sample, and dried the subsamples for 3 days at 60 degrees C, then weighed them again to determine water volume, assuming 1g/ml as the water density. We calculated the volumetric soil water content using the formula: volumetric soil water content = water volume (ml)/total soil volume (ml). b. Instrumentation: Method 1: none Method 2: none Method 3: none Method 4: Muffle furnace Method 5: none Method 6: Refractometer Manufacturer: Vista (Model: A355ATC) Parameter: Salinity (Accuracy: 1 PSU, Readability: 1 PSU, Range: 0-100 PSU) Method 7: none Method 8: none Method 9: none c. Taxonomy and Systematics: Method 1: not applicable Method 2: not applicable Method 3: not applicable Method 4: not applicable Method 5: not applicable Method 6: not applicable Method 7: not applicable Method 8: not applicable Method 9: not applicable d. Permit History: Method 1: not applicable Method 2: not applicable Method 3: not applicable Method 4: not applicable Method 5: not applicable Method 6: not applicable Method 7: not applicable Method 8: not applicable Method 9: not applicable 4. Project Personnel a. Personnel: 1: Hongyu Guo 2: Steven C. Pennings 3: Jane Buck 4: Christine Ewers 5: Daniel F. Saucedo 6: Jacob Shalack 7: Kazimierz Wieski 8: Yihui Zhang b. Affiliations: 1: University of Houston, Houston, Texas 2: University of Houston, Houston, Texas 3: University of Houston 4: Christian-Albrechts University 5: University of Georgia Marine Institute, Sapelo Island, Georgia 6: University of Georgia Marine Institute, Sapelo Island, Georgia 7: University of Houston, Houston, Texas 8: Xiamen University III. Data Set Status and Accessibility A. Status 1. Latest Update: 19-May-2014 2. Latest Archive Date: 19-May-2014 3. Latest Metadata Update: 19-May-2014 4. Data Verification Status: New Submission B. Accessibility 1. Storage Location and Medium: Stored at GCE-LTER Data Management Office Dept. of Marine Sciences Univ. of Georgia Athens, GA 30602-3636 USA on media: electronic data download (WWW) or compact disk 2. Contact Person: Name: Wade M. Sheldon, Jr. Address: Dept. of Marine Sciences University of Georgia Athens, Georgia 30602-3636 Country: USA Email: sheldon@uga.edu 3. Copyright Restrictions: not copyrighted 4. Restrictions: All publications based on this data set must cite the contributor and Georgia Coastal Ecosystems LTER project, and two copies of the manuscript must be submitted to the GCE-LTER Information Management Office. a. Release Date: Affiliates: May 20, 2014, Public: May 20, 2014 b. Citation: Data provided by the Georgia Coastal Ecosystems Long Term Ecological Research Project, supported by funds from NSF OCE 0620959 (data set PLT-GCET-1405) c. Disclaimer: The user assumes all responsibility for errors in judgement based on interpretation of data and analyses presented in this data set. 5. Costs: free electronic data download via WWW, distribution on CD may be subject to nominal processing and handling fee IV. Data Structural Descriptors A. Data Set File 1. File Name: PLT-GCET-1405_Field-Survey_1_0.CSV 2. Size: 50 records 3. File Format: ASCII text (comma-separated value format) 3a. Delimiters: single comma 4. Header Information: 5 lines of ASCII text 5. Alphanumeric Attributes: 6. Quality Control Flag Codes: 7. Authentication Procedures: 8. Calculations: Shell Content: shell content = oyster shell dry mass (g) / soil total dry mass (g) Volumetric Water Content: volumetric water content = water volume (ml) / total soil volume (ml) 9. Processing History: Software version: GCE Data Toolbox Version 3.8.0 (09-Nov-2013) Data structure version: GCE Data Structure 1.1 (29-Mar-2001) Original data file processed: datasheet1.txt (50 records) Data processing history: 19-May-2014: new GCE Data Structure 1.1 created ('newstruct') 19-May-2014: 50 rows imported from ASCII data file 'datasheet1.txt' ('imp_ascii') 19-May-2014: 81 metadata fields in file header parsed ('parse_header') 19-May-2014: data structure validated ('gce_valid') 19-May-2014: Q/C flagging criteria applied, 'flags' field updated ('dataflag') 19-May-2014: Name of column Vegetation Zone changed to Vegetation_Zone; Name of column Sampling Year changed to Sampling_Year; Name of column Sampling Month changed to Sampling_Month; Name of column Sample Number changed to Sample_Number; Name of column Shell Content changed to Shell_Content; Name of column Volumetric Water Content changed to Volumetric_Water_Conten ('ui_editor') 19-May-2014: imported Dataset, Project, Site, Study, Status, Supplement metadata descriptors from the GCE Metabase ('imp_gcemetadata') 19-May-2014: updated 48 metadata fields in the Dataset, Project, Site, Status, Study, Supplement sections ('addmeta') 19-May-2014: flags for columns Shell_Content and Volumetric_Water_Content converted to data columns, flag codes updated in metadata ('flags2cols') 19-May-2014: updated 6 metadata fields in the Data sections ('addmeta') 19-May-2014: updated 15 metadata fields in the Status, Data sections to reflect attribute metadata ('updatecols') 19-May-2014: parsed and formatted metadata ('listmeta') B. Variable Information 1. Variable Name: column 1. Vegetation_Zone column 2. Sampling_Year column 3. Sampling_Month column 4. Sample_Number column 5. Shell_Content column 6. Flag_Shell_Content column 7. Volumetric_Water_Content column 8. Flag_Volumetric_Water_Content 2. Variable Definition: column 1. Name of vegetation zone column 2. Year of sampling column 3. Month of Sampling column 4. Number of sample column 5. Shell content of soil total dry mass (%) column 6. QA/QC flags for Shell content of soil total dry mass (%) (flagging criteria, where "x" is Shell_Content: x<0="I", x>100="I", x<0="Q", x>90.41="Q") column 7. Volumetric water content of soil total volume (%) column 8. QA/QC flags for Volumetric water content of soil total volume (%) (flagging criteria, where "x" is Volumetric_Water_Content: x<0="I", x>100="I", x<8="Q", x>82.46="Q") 3. Units of Measurement: column 1. none column 2. YYYY column 3. MM column 4. none column 5. percent column 6. none column 7. percent column 8. none 4. Data Type a. Storage Type: column 1. alphanumeric column 2. integer column 3. integer column 4. integer column 5. floating-point column 6. alphanumeric column 7. floating-point column 8. alphanumeric b. Variable Codes: Flag_Volumetric_Water_Content: Q = questionable value c. Numeric Range: column 1. (none) column 2. 2008 to 2008 column 3. 8 to 8 column 4. 1 to 20 column 5. 0.002116 to 90.4057 column 6. (none) column 7. 8 to 82.4615 column 8. (none) d. Missing Value Code: 5. Data Format a. Column Type: column 1. text column 2. numerical column 3. numerical column 4. numerical column 5. numerical column 6. text column 7. numerical column 8. text b. Number of Columns: 8 c. Decimal Places: column 1. 0 column 2. 0 column 3. 0 column 4. 0 column 5. 2 column 6. 0 column 7. 2 column 8. 0 6. Logical Variable Type: column 1. free text (none) column 2. datetime (discrete) column 3. datetime (discrete) column 4. nominal (discrete) column 5. calculation (continuous) column 6. coded value (none) column 7. calculation (continuous) column 8. coded value (none) 7. Flagging Criteria: column 1. none column 2. x<2008="Q", x>2008="Q" column 3. x<8="Q", x>8="Q" column 4. x<1="Q", x>20="Q" column 5. x<0="I", x>100="I", x<0="Q", x>90.41="Q" column 6. none column 7. x<0="I", x>100="I", x<8="Q", x>82.46="Q" column 8. none C. Data Anomalies: V. Supplemental Descriptors A. Data Acquisition 1. Data Forms: Paper log sheets, Excel spreadsheets 2. Form Location: Lab of Steven Pennings, Dept. of Biology and Biochemistry, University of Houston, Houston TX 3. Data Entry Validation: B. Quality Assurance/Quality Control Procedures: C. Supplemental Materials: D. Computer Programs: R statistical software E. Archival Practices: F. Publications: not specified G. History of Data Set Usage 1. Data Request History: not specified 2. Data Set Update History: none 3. Review History: none 4. Questions and Comments from Users: none