1999 Progress Report: CISNet: Molecular to Landscape-Scale Monitoring of Estuarine EutrophicationEPA Grant Number: R826944
Title: CISNet: Molecular to Landscape-Scale Monitoring of Estuarine Eutrophication
Investigators: Morris, James T. , Fletcher, Madilyn , Jensen, John , Lewitus, Alan , Noble, Peter , Porter, Dwayne
Institution: University of South Carolina at Columbia
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 1998 through September 30, 2001 (Extended to September 30, 2002)
Project Period Covered by this Report: October 1, 1998 through September 30, 1999
Project Amount: $579,650
RFA: Ecological Effects of Environmental Stressors Using Coastal Intensive Sites (1998) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Environmental Statistics , Ecosystems
This research addresses three hypotheses: (1) the composition and abundance of bacterial and phytoplankton communities will differ among estuaries as functions of nutrient availability; (2) bacterial and phytoplankton communities form associations that vary in complexity (species diversity) as a function of nutrient availability; and (3) at a landscape scale, remote sensing of the concentration of chlorophyll in emergent wetland vegetation will provide a quantitative index of wetland condition, and will demonstrate differences in nutrient loading among estuaries.
The first major component of this project focuses on Hypotheses 1 and 2 above, and concerns the influences of the nutrient status of estuaries on the composition of bacterial and phytoplankton communities. Our field work is concentrated in the North Inlet estuary, South Carolina (a nutrient impoverished estuary), and the ACE Basin estuary, South Carolina (a highly fertile estuary). Samples for bacterial community composition have been collected in tandem with phytoplankton samples. Bacterial samples are being analyzed using rRNA profiling techniques (PCR/DGGE). These samples are still being processed, and the data are not yet ready for analysis. Phytoplankton community composition is being assessed by analyzing the types of photo-pigments that are present in a water sample. These data are now being analyzed. Significant progress was made on five tasks identified with this part of the project.
Task 1: Develop the HPLC Pigment Protocol To Optimize Separation of Pigments of Known Chemotaxonomic Importance. The protocol we are using makes use of High Performance Liquid Chromatography (HPLC) and is based on a method used by Van Heukelem and Thomas, which has been found to provide good pigment separation and sensitivity. Data from our HPLC analyses are included in Van Heukelem and Thomas, who have acknowledged our CISNet grant.
Task 2: Complete Phase 1 of the CHEMTAX Calibration Process, Which Included HPLC Analysis of Pigments Harvested From Monocultures of 12 Taxonomically Diverse Species of Estuarine Phytoplankton Grown at Different Physiological States. A new matrix factorization program, "CHEMTAX," has been used to relate HPLC pigment data to phytoplankton composition in the open ocean. We are in the process of applying this program to estuarine communities by calibrating it against phytoplankton monocultures isolated from estuarine environments. Thus far, HPLC chromatograms have been analyzed from 12 taxonomically diverse species acclimated to growth at saturating or limiting light, and harvested from exponential or stationary growth phases. We envision that the combined applications of CHEMTAX and neural computing to HPLC pigment profiles will lead to a major advance in current capabilities in determining phytoplankton community composition.
Task 3: Conduct Weekly Monitoring of Physical, Chemical, and Biological Variables at Two Sites Within North Inlet Estuary That Vary in Patterns of Nutrient Quantity and Quality. Water samples have been collected weekly from two sites in the North Inlet Estuary. One site is closer to the wetland forest, and therefore, is influenced by runoff and groundwater to a greater extent than the other. The correlative relationships between pigments and physicochemical properties are now being assessed. During the 1999 monitoring effort, a red tide was documented at one of our sampling sites. This was caused by a dinoflagellate species (tentatively identified as Peridinium sp.), which comprised greater than 95 percent of the total phytoplankton biomass, reaching greater than 100,000 cell mL-1. Characterizations of the red tide thus far include spatial distribution (daily, areal, diel, depth, and tidal stages) in relation to environment (nutrients, salinity, temperature, water flow, microzooplankton, and phytoplankton composition); identification; pigment composition; and physiological and life cycle factors. Although our understanding of the physiological ecology of this dinoflagellate will be improved after the completion of several of the above ongoing analyses, early results are providing important clues. For example, dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) varied inversely during the red tide, but the patterns are counterintuitive to those expected during a phytoplankton bloom, where DIC should be taken up and DOC produced. The patterns infer that DOC was assimilated during the bloom. Alternative explanations can be eliminated when the results of bioassays are known.
Task 4: Apply Neural Network (NN) and Principle Component Analyses (PCA) to the Monitoring Data To Determine Relationships Between Environmental Properties and Phytoplankton Pigment Composition. Physical data, nutrient concentrations, and phytoplankton pigments have been analyzed by both conventional and new (i.e., neural computing) statistical methods. In the first phase of the statistical analysis, we determined the major sources of variability in the data using PCA. These results suggest that salinity, DOC, dissolved organic nitrogen (DON), and temperature are important contributors to the variability of the pigments. During the first year of this project, we also developed NN software (Noble, et al., in press) that optimizes its architecture. We are using this software to perform sensitivity analyses of the data and to determine which phytoplankton pigments were associated with specific environmental variables. Using NN sensitivity analyses, we found that the pigment violaxanthin contributed most to the variation in photo-pigments, followed by chlorophyll a and chlorophyll c. Moreover, salinity, DOC, temperature, NH4, sample position in the water column, and orthophosphate (OP) had significant associations with the pigments, while other environmental variables (i.e., DIC, DON, tide height, dissolved organic phosphate [DOP], and NO3) did not. The pigment violaxanthin, an accessory pigment of green algae, had strong associations with NH4 (0.11) and orthophosphate (0.10). When the NH4 concentration was less than 18 µM and OP was low, violaxanthin concentrations were low. When the NH4 concentration exceeded 18 µM and OP was high, violaxanthin concentrations were high. The significance of this finding is that high OP and NH4 concentrations are associated with high concentrations of violaxanthin. Developing NNs that can recognize the association between HPLC pigment profiles and phytoplankton taxonomy will allow us to forecast environmental conditions that promote different community types, including red tide blooms.
Task 5: Sample Several Sites in North Inlet and the ACE Basin (Estuaries That Differ in Fertility) During the Phytoplankton Bloom Period (Summer) To Determine the Relationship Between Phytoplankton and Nutrient Quantity and Quality. In general, nutrients, phytoplankton biomass, and community structure differed between estuaries, and these differences were greatest following a storm event (i.e., high nutrient loading). In sites where salinity was greater than 24 ppt, water column chlorophyll concentrations did not differ between estuaries in July and August, but were significantly higher at all ACE Basin sites in September. This trend was similar to that of inorganic nutrients, which generally were much greater in the ACE Basin than in North Inlet in September. However, DON, DOP, DOC, and Si did not vary consistently between basins. The higher nutrient concentrations in ACE Basin in September likely were the result of Hurricane Floyd's influence. Similar high nutrient concentrations were not observed in North Inlet, presumably because this estuary has a small watershed. Some differences in the HPLC pigment profiles between these estuaries are noteworthy. For example, zeaxanthin (a carotenoid found in cyanobacteria) and alloxanthin (a marker carotenoid for cryptophytes), which generally comprised a similar proportion of the biomass in both estuaries in July, contributed a lower percentage of pigment biomass in the ACE Basin than in North Inlet in September, while the contribution of fucoxanthin, a carotenoid considered a marker for diatoms (this must be confirmed by microscopy) was relatively greater in the ACE Basin in September. These patterns are consistent with the dogma that diatoms generally respond favorably to high nutrient (especially NO3) inputs, while marine cyanobacteria (Synechococcus spp.) are adapted to oligotrophic conditions.
A second component of this project is developing linkages between remotely sensed images of coastal wetlands, fertility or nutrient status, and the chlorophyll biomass of the emergent wetland plant community. In situ radiometric and biophysical (chlorophyll and biomass) data obtained at approximately 50 locations within each Spartina alterniflora-dominated study area will be used to develop regression models between the biophysical data and remote sensor data. These models will be used to produce "greenness" maps depicting the functional health and productivity of the estuarine wetland vegetation. Second, by using this technique, we will develop maps of functional ecosystem health for each year of the study, and perform a change detection analysis between 1999 and 2001. This will provide baseline data for long-term monitoring of coastal estuarine wetlands, and the techniques we are developing should be applicable to other coastal regions of the United States and the world having similar phenological characteristics. Significant progress has been made on the following four tasks:
Task 1: Obtain and Classify Remotely Sensed Imagery of the Study Area (To Coincide With Field Sampling) During the Spring and Late Summer. The original proposal called for the acquisition of Thematic Mapper remotely sensed imagery of the North Inlet-Winyah Bay study area during periods corresponding to peak biomass and chlorophyll concentrations. Following preliminary data analysis of biophysical characteristics of several estuaries in South Carolina, it was decided to expand the study area to include portions of the ACE Basin estuary located south of Charleston, South Carolina. High spatial resolution (50 cm to 3 m), multispectral ADAR images of the ACE Basin, South Carolina study area were taken on September 23, and of the North Inlet, South Carolina study area on November 4, 1999. The ADAR 5500 is a fully digital four-band multispectral system operated by Positive Systems and offers 400 percent more ground coverage per scene than digital video systems. Able to capture both color and color infrared images simultaneously, the ADAR System 5500 acquires digital photographs in four configurable spectral bands between 400 nm and 1,000 nm. All digital orthomosaics of ADAR digital aerial photography are produced to National Map Accuracy Standards (1:24,000, +/- 40 feet), which ties each pixel to an x,y ground coordinate and adjusts the image for terrain relief. We have not yet received either data set, but have been recently provided hardcopy samples.
Task 2: Collect Spectral Response Data and Plant Biophysical Data for Use in Developing Relationships Between Chlorophyll and Spectral Reflectance of Spartina alterniflora and Productivity Models. It was hypothesized that we can develop mathematical relationships between in situ biophysical data (e.g., standing chlorophyll biomass and CO2 exchange) and the brightness values associated with the remotely sensed imagery. A series of transects were established within the estuarine component of the study areas. Canopy radiance data, leaf area index (LAI), chlorophyll, and CO2 exchange data were collected in the ACE Basin study area during July, August, and September. Data were collected in the North Inlet study area during July and November. Canopy radiance data were collected using a 350 nm to 1,050 nm field spectroradiometer. The geographical (x,y) location of each sample was recorded using a GPS. These biophysical data will be correlated with canopy radiance data collected using the field spectrometer and with remotely sensed data.
Task 3: Correlate Biophysical and Remote Sensing Data. We have not received the remotely sensed data to allow us to correlate the biophysical and remote sensing data, though we have made significant progress in calibrating CO2 exchange data to standing chlorophyll biomass. When complete, a CO2 exchange model will allow users to map primary production across coastal wetland landscapes using remote sensor data as inputs to the model.
Task 4: Maintain Databases and Develop and Maintain Metadata. All biophysical field data collected by the remote sensing group has been entered into an Excel spreadsheet, QA/QC performed, and backups generated. Spectral response data have been downloaded, reviewed, and backups generated. GPS data collected at each sampling location either has been, or is in the process of being entered into our GIS. These data layers also will be backed up. Metadata generation is underway.
With respect to the first component of our project, nutrient controls on phytoplankton and bacterial community composition, our strategy for the next year is to relate ecological dynamics to community structure (Noble, et al., abstract for ASM General Meeting 2000) and to determine the relationship between HPLC pigment profiles and taxonomic affiliations. We expect to have the first rRNA measurements of bacterial communities available for analysis. Understanding the association between ecological dynamics and microbial community structure will allow us to develop unifying principles about ecosystem resilience and stability.
The landscape-scale, remote-sensing component of the project will focus on analyzing the previous year's remotely sensed data, and on continuing to develop the biophysical database. In addition, we have established a relationship with NASA that will allow us to research the effects of bidirectional reflectance on image classification and information extraction. The bidirectional reflectance distribution function (BRDF) is a theoretical concept that describes the relationship between: (1) the geometric characteristics of the irradiance, and (2) the sensor viewing geometry. An understanding of BRDF is needed in remote sensing for the correction of view and illumination angle effects (for example, in image standardization and mosaicing), deriving albedo, improving land cover classification accuracy, enhancing cloud detection, and correcting atmospheric conditions. Future data needs will require a clear understanding of the effects of BRDF to properly calibrate and apply new forms of data in remote sensing applications. To better understand the impact of BRDF when acquiring high spatial resolution imagery of an estuarine environment, the Commercial Remote Sensing Verification and Validation (V&V) Team at the National Aeronautics and Space Administration (NASA) Stennis Space Center has provided us with access to a custom field goniometer adapted from the Swiss Field Goniometer System (FIGOS). NASA will repeat the SFG measurements at our North Inlet study location in early 2000.
The remote sensing component of the study also identified a fifth task, to perform an annual change detection analysis of estuarine land cover and chlorophyll content in Spartina alterniflora, which is scheduled to begin during Year 2.
Journal Articles on this Report : 3 Displayed | Download in RIS Format
|Other project views:||All 63 publications||15 publications in selected types||All 15 journal articles|
||Claustre H, Hooker SB, Van Heukelem L, Berthon J, Barlow R, Ras J, Sessions H, Targa C, Thomas CS, Van Der Linde D, Marty JC. An intercomparison of HPLC phytoplankton pigment methods using in situ samples: application to remote sensing and database activities. Marine Chemistry 2004;85(1-2):41-61.||
||Lewitus AJ, Koepfler ET, Pigg RJ. Use of dissolved organic nitrogen by a salt marsh phytoplankton bloom community. Archiv fur Hydrobiologie Special Issues, Advances in Limnology 2000;55:441-456.||
||Noble PA, Almeida JS, Lovell CR. Application of neural computing methods for interpreting phospholipid fatty acid profiles of natural microbial communities. Applied and Environmental Microbiology 2000;66(2):694-699.||