National Park Service

South Florida/Caribbean I&M Network (SFCN)

Nutrient Dynamics & Water Chemistry Monitoring

Monitoring Reports

Nutrient Dynamics & Water Chemistry Data

Protocol

  • A SFCN-wide nutrient dynamics & water chemistry monitoring reporting strategy is currently under development.

For more information contact:

Andrea Atkinson, Ph D.
Michael Feeley, Ph D. Kevin R. T. Whelan, Ph D.

Importance/Issues

Water quality is critical to a healthy ecosystem. Nutrients and physical characteristics within freshwater and marine water bodies drive primary production and when unbalanced can have deleterious effects. Understanding water quality allows more complete interpretation of other indicators. The Greater Everglades system is historically oligotrophic (very low nutrients) and any additional nutrients is severely disruptive to vegetation (resulting in cattail and exotics overgrowth) and altering the periphyton at the base of the food web. In the marine system excessive nutrients result in algal blooms, algal overgrowth of coral reefs, and disrupt seagrass communities. Nutrients can change due to numerous reasons, for example; upstream/upland development, agricultural inputs, malfunctioning septic systems, boat discharges, atmospheric deposition, as well as internal nutrient cycling. Nutrient enrichment in freshwater and brackish areas has occurred primarily due to agricultural inputs (South Florida, St. Croix- Salt River Bay) with some impacts due to malfunctioning septic systems (Virgin Islands National Park and Florida Keys). Rain events can create pulses of nutrients. CERP/MOD Waters Everglades restoration is expected to reduce nutrient inputs to the Greater Everglades system.

Monitoring Objectives

  • What are the status and trends in the spatial and temporal distributions of nutrients at specific sites in the wet prairies and marshes, near shore areas, and marine water bodies?
  • What are the status and trends in nutrient loading to the estuaries from all sources and in sediment loading to guts and standing ephemeral pools at VIIS?
  • What are the status and trends in the spatial and temporal distributions of physical water chemistry (e.g., conductivity, DO, temperature, pH, etc.) in the wet prairies and marshes, near shore areas, and marine water bodies?

Status and Trends

CERP RECOVER 2009 System Status Report has a summary of Greater Everglades Oligotrophic Nutrients Results and Southern Coastal Systems Water Quality Results. SFCN has funded a post-doc position in cooperation with Joe Boyer and Jim Fourquerean of Florida International University Other useful NPS, EPA, and SFWMD Regional Summary Reports (see list of monitoring reports at the top of this page) are available that analyze watershed issues over longer time periods (based upon multi-agency data). Some water quality results are presented under other vital signs:

  • Marine Benthic Communities (reef water temperatures)
  • Estuarine Salinity Patterns
  • Periphyton
  • Phytoplankton (marine algal blooms)
  • Air Quality (deposition)

Dr. Darrell Herbert was hired to work on the Nutrient Dynamics and Water Chemistry vital signs via a cooperative agreement with Jim Fourqurean at Florida International University. Dr. Herbert is currently developing a statistical model that will predict the quality of benthic estuarine and marine habitat characterized by submerged aquatic vegetation (SAV, Table 1) on the basis of commonly measured water quality parameters (Table 2). The methodology is similar to that used in earlier smaller-scale models focused on Florida Bay (Fourqurean et al. 2003, Herbert et al. 2011*). Geo-referenced water quality and benthic habitat data (Figure 1) from multiple public and private sources (Table 3) have been quality checked, quality assured, and synthesized into a single database spanning a period of more than 20 years.

Table 1. Submerged Aquatic Vegetation (SAV).
Seagrasses Macroalgae Brackish Vascular
T. testudinum Udotea spp. Chara species
S. filiforme Acetabularia spp. Utricularia species
H. wrightii Penicillus spp. Najas marina
H. decipiens Halimeda spp.  
H. engelmanni Caulerpa spp.  
H. johnsonii Other Chlorophyta  
R. maritima Rhodophyta, non-drift  
  Phaeophyta, non-drift  
Table 2. Water quality data.
SAV Sample Depth pH Nitrate (NO3)
Salinity Chlorphyll-a Nitrite (NO2)
Temperature Soluble reactive P Ammonia (NH4)
Dissolved Oxygen Total Dissolved P Dissolved Inorganic N
Turbidity Total Organic C Total Organic N
Light attenuation Silicate Total N
Table 3. SAV and water quality data sources.
SAV Sources Water Quality Sources
Florida Audubon Society Atlantic Oceanic & Meteorological Laboratory (NOAA)
Florida Coastal Everglades LTER program (NSF) Miami-Dade Dept. of Environmental Resources Management
Florida Bay Fisheries Habitat Assessment Program South Florida Water Management District
Miami-Dade Dept. of Environmental Resources Management Southeast Environmental Research Center (FIU-SERC)
Seagrass Ecosystems Research Laboratory (FIU-SERC)  
Shallow Water Positioning System (UM/RSMAS-SWaPS)  
South Florida Water Management District  
Locations of water quality and SAV stations.  Some SAV stations double as water quality stations for variables including salinity, temperature, pH, turbidity, light attenuation, and dissolved oxygen
Figure 1. Locations of water quality and SAV stations. Some SAV stations double as water quality stations for variables including salinity, temperature, pH, turbidity, light attenuation, and dissolved oxygen.

Because of spatial separation of SAV and water quality stations, geo-statistical interpolations of water quality data were performed to derive precise estimates of each water quality parameter at the coordinates of SAV stations. The product of the interpolations is an Arc-GIS geo-database with 289 rasters containing derived water quality data on a 100 m grid. These rasters are being used to develop the predictive SAV statistical model. The geo-database has additional value in that it can support a wide variety of other natural resource evaluations.

The project is now at the stage where statistical analyses are in progress with the assistance of Scott Mize, a USGS Hydrologist in Baton Rouge, and Dorothy Sifuentes, a USGS Supervisory Hydrologist in Fort Lauderdale. In the first set of analyses, principal component analysis (PCA) was used to reduce the number of water chemistry parameters necessary to build statistical relationships between water quality and benthic habitat (Clark 2001*). PCA demonstrated that 96.4% of the variance in water quality chemistry is accounted for with 5 variables; salinity, NOx , total nitrogen, total phosphorus, and total organic carbon. Salinity variance and the fraction of light reaching the benthos will be added to the five chemistry variables as predictors in the SAV statistical model. The next step, now in progress, uses multi-dimensional scaling and discriminant function approaches to build the statistical relationships between benthic habitat and water quality.

The primary product is a predictive statistical model that uses water quality data to forecast changes in the benthic habitat characteristics. The model can be used as a management tool to interpret trajectories in previously collected water quality and benthic habitat data from a regional decadal sampling effort and allow a clear determination of predicted outcomes based on past trends and their relationships. This will allow for a move from responsive management decisions to forward-thinking, adaptive management actions with a clear tool to communicate the reasons behind the management decision. Additionally, it is envisioned that the geo-databases developed in the execution of this project will support a wide variety of other natural resource evaluations, such as habitat suitability and forage availability for wading and diving seabirds, important game fish, and pink shrimp populations in Biscayne, Everglades, and Dry Tortugas National Parks.

Approach

Link to existing monitoring and reporting

A large amount of existing monitoring is being conducted by Everglades National Park, Big Cypress National Preserve, the South Florida Water Management District, the U.S. Geological Survey, and is made available through the DataForEVER database maintained by Everglades National Park and in DBHydro maintained by the South Florida Water Management District. Thus SFCN’s strategy is to link to these existing monitoring programs, data streams, and summaries reports where possible.

Fund post-doc to examine relationships between water quality and benthic community characteristics

This project’s objectives are to determine how best to link to and report the status and long-term trends in water quality parameters associated with the SFCN Estuarine Salinity, Nutrient Dynamic and Submerged Aquatic Vegetation vital signs but also evaluate relationships between water quality metrics and response variables including seagrass blade nutrient data and seagrass community data and determine critical thresholds in water quality parameters. Subsequently, community data will be used to develop predictive, probabilistic models of community change in response to water quality conditions.

Assist Virgin Islands National Park with analysis and reporting

Virgin Islands National Park has been implementing a water quality monitoring program since 2001 in which 16 biannual nutrient grab samples are collected by park staff and sent for analysis. However the results have not been regularly reported. SFCN will review procedures and if possible assist park staff with developing analysis and reporting procedures.

*References:

  • Clark, KR, and Gorley, RN (2006) PRIMER v6: User Manual/Tutorial. Plymouth, England
  • Fourqurean, J.W., Boyer, J.N., Durako, M.J., Hefty, L.N. & Peterson, B.J. (2003) Forecasting responses of seagrass distributions to changing water quality using monitoring data. Ecological Applications. 13:474–489.
  • Herbert, DA, Perry, WB, Cosby, BJ, and Fourqurean, JW. (2011). Projected reorganization of Florida Bay seagrass communities in response to increased freshwater inflow with Everglades restoration. Estuaries and Coasts 34:973–992.
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