Abstract:
Quantifying the linkages between primary production and higher trophic levels is necessary to understand why particular regions can support high fisheries production. Modified dilution experiments were employed to characterize microbial communities in surface waters at four sites from within a bay to the shelf in the northern Gulf of Mexico (nGOM). Inshore surface waters were more variable than shelf surface waters due to the strong influence of river discharge. Phytoplankton (Chl a) and prokaryote biomass were both significantly higher inshore than on the shelf, with phytoplankton significantly higher than prokaryotes inshore. Virus and heterotrophic nanoflagellate abundances, however, did not differ between inshore and shelf waters. Samples were amended with nutrients (N + P) to examine the impact of nutrient limitation. Prokaryotes were nutrient limited in 14 (28%) of the experiments, while phytoplankton were nutrient limitated in 26 (52%) of the experiments. When phytoplankton were nutrient limited, prokaryote growth rates were significantly altered. A similar impact on phytoplankton growth rates occurred when prokaryotes were nutrient limited, suggesting that the two groups are in competition for resources. Grazing was detected in the majority of experiments, while viral lysis was only detected in 24% of phytoplankton and 12% of prokaryote experiments. Growth and grazing rates for both phytoplankton and prokaryotes were tightly coupled inshore and on the shelf, with significantly more phytoplankton and prokaryotes grazed inshore (average = 106% and 75%, respectively) than on the shelf (average = 55% and 57%). These findings indicate that surface waters across the estuary are highly productive, with microzooplankton grazing transferring the majority of the microbial production to higher trophic levels. Permission to access these data must be given by Dr. Alice Ortmann of the Dauphin Island Sea Lab. Acknowledgment of the Dauphin Island Sea Lab (DISL), Fisheries Oceanography of Coastal Alabama (FOCAL) program, and the Alabama Department of Conservation and Natural Resources is required in products developed from these data, and such acknowledgment as is standard for citation and legal practices for data source is expected by users of these data. Users should be aware that comparison with other data sets for the same area from other time periods may be inaccurate due to inconsistencies resulting from changes in mapping conventions, data collection, and computer processes over time. The distributor shall not be liable for improper or incorrect use of these data, based on the description of appropriate/inappropriate uses described in the metadata document. These data are not legal documents and are not to be used as such.
Suggested Citation:
Ortmann, Alice. 2015. Interactions between members of the microbial loop in an estuary dominated by microzooplankton grazing, Mobile Bay and Shelf, 2009-2011.. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N7KS6PJB
Data Parameters and Units:
Water Sample data: Experiment number (numeric) Month collected (month) Location, The station on the FOCAL transect where the water sample was collected [name latitude (decimal degrees) longitude (decimal degrees) depth (meters)] [T35 29.79887 -88.20828 35 CP 30.0902 -88.2116 19.5 DI 30.25345 -88.04355 6 MB 30.4374 -88.0113 3] Year collected (year) season collected, defined based on ambient temperatures (spring, summer, fall, winter) Chl a Process, The dominant loss process of phytoplankton, measured as clhorophyll a (biomass loss was due to lysis, biomass was due to grazing, both, or no biomass loss) Chl a Nutrient Limitation, Whether phytoplankton were nutrient (N/P) limited (Y=Nutrients limited growth, N=Nutrients did not limit growth) HP Process, The dominant loss process of heterotrophic prokaryotes (biomass loss was due to lysis, biomass was due to grazing, both, or no biomass loss) HP Nutrient Limitation, Whether prokaryotes were nut rient limited (Y=Nutrients limited growth, N=Nutrients did not limit growth) Location, The coastal zone where the water sample was collected (Inshore=inside Mobile Bay, Shelf=on the continental shelf outside of the bay) Temp, Water temperature as measured by CTD (degrees Celsius) Salinity, as measured by CTD (ppt) DO, Dissolved oxygen as measured by CTD (mg/L) DIN, Amount of dissolved inorganic nitrogen in the water sample (uM microMole) PO4, Amount of dissolved phosphate in the water sample (uM) DSi, Dissolved organic silicon in the water sample (uM) DON, Amount of dissolved organic nitrogen in the water sample (uM) N:P, Ratio of nitrogen to phosphorus in the water sample (dimensionless) Flow, Estimated discharge into Mobile Bay (m^3/seconds) Chl a, Concentration of chlorophyll a in whole water, measured as a proxy for phytoplankton (ug/L) Phytoplankton, Phytoplankton biomass, converted assuming 50 micrograms of carbon per microgram of chlorophyll a (50 ugC/ug Chla) (ug C/L! iter) HNF Heterotrophic nanoflagellates per milliliter (number of cells/ml) Prokaryotes (number of cells/milliliter) Prokaryote biomass, converted assuming 30 fg C/cell (ug*C/L) Viruses (number of cells/milliliter) Loss of phytoplankton per day due to grazing (number of cells/milliliter) 95% confidence interval for phytoplankton grazing rate Loss of phytoplankton per day due to lysis (number of cells/milliliter) 95% confidence interval for phytoplankton lysis rate Growth rate of phytoplankton (number of cells/milliliter) 95% confidence interval for phytoplankton growth rate Grazing rate on prokaryotes (number of cells/milliliter) 95% confidence interval for prokaryote grazing rate Lysis rate of prokaryotes (number of cells/milliliter) 95% confidence interval for prokaryote lysis rate Growth rate of prokaryotes (number of cells/milliliter) 95% confidence interval for prokaryote growth rate Growth of phytoplankton minus grazing and lysis (%) Growth of prokaryotes minus grazin! g and lysis (%)
Methods:
Process_Description: Collection: Samples (26L) were collected from surface waters from four FOCAL stations (MB, DI, CP and T35), filtered through 150 um Nitex screen and kept in the dark until returned to the DISL. Physical parameters were obtained from CTD casts. Samples were processed in low light for nutrients, abundance of protists, prokaryotes and viruses and the concentration of chlorophyll a. Modified dilution experiments were carried out to determine the growth, grazing and viral lysis rates for phytoplankton and prokaryotes. Process_Description: Nutrients were measured using a Skalar + autoanalyzer. Samples were collected after filtration through a 0.7 um glass fiber filter. Samples were stored frozen prior to processing. Process_Description: Abundances: Phytoplankton was measured using chlorophyll a. Water was filtered through a 25 mm 0.7 um glass fiber filter and frozen immediately in liquid nitrogen, then stored at -86C until processing. Pigments were extracted in acetone/DMSO and determined using a Turner Fluoromet! er. Samples for protists, prokaryotes and viruses were fixed in 0.5% EM grade glutaraldehyde and flash frozen in liquid nitrogen. Samples were stored at -86C until processing. DAPI slides were prepared to determine protist numbers using epifluorescences microscopy while prokaryotes and viruses were counted using flow cytometry. Process_Description: Modified Dilution Experiments: To determine rates of growth, grazing and viral lysis, water was filtered through 0.7 um glass fiber filter and a 0.2 um Durapore filter. Virus free water was produced by passing the filtered water through a tangential flow filtration cartridge (30kDa). Two parallel dilution series were produced in triplicate. One with 0.2 um filtered water and one with virus free water. Samples were collected for chlorophyll a and prokaryote abundance at t=0 and t=24. Prokaryotes were also sampled at t=12. 1 L bottles were incubated in floating cages in the water to allow for ambient light and temperature condition! s. Growth, grazing and viral lysis rates were determined from changes in the abundance of pigments or cells over time using linear regressions.
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