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Development Prototype: Puget Sound Ecosystem Portfolio Model

Shellfish Pollution Model

The Shellfish Pollution Model - estimates fecal coliform bacteria concentrations in commercial shellfish growing areas based on scenarios of land cover change in drainages

Shellfish are a culturally and economically valued ecosystem component in Puget Sound (Dethier 2006). Across Washington in 2005, commercial shellfish harvesting was a $97 million industry (Chew and Toba 2005), and over 450,000 recreational shellfish licenses were sold (PSAT 2007). For Native Americans, shellfish have always been a key domestic and commercial product, and are used for subsistence, economic, and ceremonial purposes. Across the country, coastal urbanization has been closely related to contamination and closure of shellfish growing areas due to bacterial contamination (Glasoe and Christy 2004). In Puget Sound’s rural, shellfish-rich counties, rapid population growth is increasing the risk for increased closures in commercial shellfish growing areas and recreational shellfish beaches (WOFM 2002). Nonpoint source pollution is the most common cause of shellfish classification downgrades in Puget Sound, where commercially approved acreage has been reduced by 25% since 1980. Also by 2006, 20% of Puget Sound recreational shellfish beaches were closed due to fecal pollution. Leading nonpoint sources include failing on-site sewage systems, farm animal wastes and stormwater runoff (WDOH 2004; PSAT 2002, 2000). For shellfish consumers, these pollutants increase the risk of disease from Noroviruses and the Hepatitis A virus (NRC 1999

The Washington State Department of Health (DOH) Office of Shellfish and Water Protection is responsible for evaluating commercial shellfish growing areas to determine their suitability for harvest, which are classified as Approved, Conditionally Approved, Restricted or Prohibited. Classification standards are derived from the National Shellfish Sanitation Program Guide for the Control of Molluscan Shellfish (Chapter IV, 2005 Revision). For a growing area to be classified as Approved, marine water samples must meet a two part water quality standard: 1) Concentration of fecal coliform bacteria (the indicator organism) cannot exceed a geometric mean of 14 per 100 ml and 2) The estimated 90th percentile cannot exceed 43 organisms per 100 ml.

The Shellfish Pollution Model evaluates the ENVISION (http://envision.bioe.orst.edu/caseStudies.htm) scenarios to determine which Puget Sound commercial shellfish growing areas are at greater risk of increased pathogen contamination within the next 50 years, given expected development patterns in watersheds draining to the nearshore (Figure 1). This statistical model relates land cover within drainages and environmental variables to fecal coliform bacteria concentration data collected by DOH. While fecal coliform bacteria are generally not harmful, their presence in high concentrations indicates that illness-causing pathogens may also be present.

Figure 1. Diagram of shellfish pollution scenario analysis

Statistical model of 2001 fecal coliform count data based on 2001 landcover data, water temperature and water salinity

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Data from ENVISION landcover change scenarios:

Managed Growth (MG)

Status Quo (SQ)

Unconstrained Growth (UG)

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Scenarios of fecal coliform counts in commercial shellfish growing areas out to 2060

A negative binomial regression method was applied to relate watershed variables and water temperature and salinity data to fecal coliform count data. Negative binomial regression is a generalized linear model suitable to count data where the variance is much greater than the mean (the case with the DOH water quality data). A backward-elimination regression analysis using a bootstrap method for standard error estimation was applied for two scales of analysis: 1) watershed – data on the entire watershed area draining to the water quality station, and 2) stream – data on the land area within 90 meters of a stream or canal/ditch within the watershed. Streams and canal/ditches were identified in the USGS National Hydrography Dataset Plus (NHD+).

Land cover change data from ENVISION scenario model outputs were used to predict new bacteria counts by watershed, and standard errors of the predictions were also calculated. Predictions were made for each scenario and each decade, for a total of seven sets of predicted values. Scenarios were Managed Growth (MG), Status Quo (SQ), and Unconstrained Growth (UG). Differences in predicted pathogen counts were calculated for the following scenario comparisons: UG – SQ 2060, UG – SQ 2030, UG – MG 2060, UG – SQ 2030, SQ – MG 2030, and SQ – MG 2060.

A watershed-scale four variable model was found to have the best model fit (Wald chi2 = 98.02, p=0.00, n=335) (Table 1). Significant variables included water salinity, water temperature, percent cover impervious surface, and percent cover evergreen forest. Fecal coliform bacteria counts increased with higher percent cover impervious surface (Figure 2) and higher water temperatures, and lower percent cover evergreen forest (Figure 3) and lower water salinity. While model results are significant, much of the variance in the data is unexplained (pseudo R2 = 0.12). Based on ENVISION model scenario projections for evergreen forest cover and impervious surfaces, predicted pathogen counts across all subbasins and years tend to be higher in the Unconstrained Growth scenario. Greater differences across scenarios were found in Bellingham Bay, Bainbridge Island, Hood Canal, and near Clallam Bay in the Strait of Juan de Fuca (see Difference Maps).

 

Table 1. Negative binomial regression results: predicted fecal coliform bacteria counts

 

Observed Coeffficient

Bootstrap Standard Error

Z

P>z

Normal-based

[95% Conf. Interval]

 

% Cover impervious surfaces

 0.025

0.011

 2.280

0.022

 0.004

 0.047

% Cover evergreen forest

-0.004

0.002

-2.610

0.009

-0.007

-0.001

Water salinity (ppt)

-0.089

0.013

-6.990

0.000

-0.114

-0.064

Water temperature (C)

 0.128

0.044

 2.890

0.004

 0.041

 0.215

Intercept

 2.211

0.600

 3.680

0.000

 1.035

 3.388

 

Figures 1 and 2: Predicted fecal coliform bacteria counts by impervious cover and by evergreen forest cover, holding other variables constant .

Chart image Chart image

 

References

Chew, K. and D. Toba. Pacific Coast Shellfish Growers Association Shellfish Production on the West Coast. Western Regional Aquaculture Center. Department of Commerce; Washington State Department of Fisheries; California Aquaculture Association.

Dethier, M. N. 2006. Native shellfish in nearshore ecosystems of Puget Sound. Puget Sound Nearshore Partnership Report No. 2006-04. Published by Seattle District, U.S. Army Corps of Engineers, Seattle, Washington.

Glasoe S. and A. Christy. 2004. Literature Review and Analysis: Coastal urbanization and microbial contamination of shellfish growing areas. Puget Sound Action Team, Olympia WA. 28 pp.

National Research Council. 1999. From Monsoons to Microbes: Understanding the Ocean’s Role in Human Health. Committee on the Ocean’s Role in Human Health, National Research Council. National Academy Press. Washington, D.C. 144 pp.

Puget Sound Action Team. 2007. 2007 Puget Sound Update: Ninth Report of the Puget Sound Assessment and Monitoring Program. Chapter 5. Nutrients and Pathogens. Puget Sound Action Team. Olympia, Washington. 260 pp.

Puget Sound Action Team. 2002. Puget Sound’s Health 2002. Puget Sound Action Team. Olympia, WA. 16 pp.

Puget Sound Action Team. 2000. Puget Sound Water Quality Management Plan. Puget Sound Action Team. Olympia, WA. 148 pp.

Washington State Department of Health. 2004. 2003 Annual Inventory: Commercial and Recreational Shellfish Areas of Washington State. Office of Food Safety and Shellfish Programs, Washington Department of Health. Olympia, WA. 36 pp.

 

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