MERGANSER - An empirical model to estimate fish and loon Hg in New England lakes

Transcription

1 MERGANSER - An empirical model to estimate fish and loon Hg in New England lakes DTMC webinar, September 6, 2011

2 Project Team

3 Building MERGANSER 1. Choose base map: National Wetlands Inventory (NWI) 2. Select lakes > 8 ha = 4404 lakes; reconcile with NHD-Plus 3. Delineate watershed boundaries for each lake 4. Assemble continuous data layers and calculate for each lake National Land Cover Dataset (NLCD) Wetland categories (NWI) Climate Topography Watershed alkalinity 5. Assemble fish fillet Hg and Loon Hg (Female Loon Units, FLU) 6. Least squares multiple regression, add and remove predictors 7. For fish: user-input of species and length 8. For loon: output in FLU

4 MERGANSER lakes

5 MERGANSER philosophy 1. In order to apply to all New England lakes, use continuous spatial data layers that can be derived for all 4404 lakes 2. Avoid water quality metrics known to correlate with Hg (e.g. ph, DOC) because not available for all lakes. 3. However, may use proxies for water quality..

6 Watershed alkalinity Proxy predictors: For example, Alkalinity for lake acidity µeq/l < >400

7 Hg Deposition model for MERGANSER Developed specifically for MERGANSER by Eric Miller, Ecosystems Research Group, Ltd., building from his earlier work (Miller et al., 2005, Ecotoxicology). Hybrid of High-Resolution Deposition Model (HRDM) and Regulatory Modeling System for Aerosols and Deposition (REMSAD)

8 Hg Deposition model for MERGANSER REMSAD 36-km resolution Modeled air and precipitation Hg concentrations HRDM 100-m resolution Uses topography, weather/climate, receptor surface, proximity to sources - dep. velocities Merging HRDM and REMSAD yields fine-scale coverage, improves on sparse real data.

9 Modeled Hg wet and dry deposition Wet Dry

10 Fish and loon Hg distribution

11 Why include loons? Provides a wildlife health endpoint to the model. Capturing loons for Hg measurement is difficult, expensive work. Need tool to predict loon Hg for lakes that have no data. Identification of potential hot spots for loon Hg.

12 Predictors of fish and loon Hg (tested)/(accepted) Spatial predictors User-specified Lake characteristics (7)/(0) Watershed Characteristics (8)/(4) Drainage area, slope, watershed alkalinity, population Climate deposition,& chemistry (11)/(2) Total Hg deposition, mean air temperature Land cover (25)/(3) % forest canopy, % agricultural, % total wetland Ecoregions (5)/(0) Interaction terms (20)/(3) Fish and Loon (15)/(11) Loon (binary) Fish length, 9 species lengths

13 Species specific variables measured for calibration and user-supplied for prediction Loon - Binary Variable (0,1) Fish Length in millimeters (mm) Fish Species (0,1) X Fish Length (mm)

14 Model statistics Number of Observations 3723 (253 loons) Number of Predictors 23 R-Square Root Mean Square Error 0.509

15 MERGANSER Residual Distribution

16 Spatial predictors Units Coefficient p value Intercept <.0001 ln (Total Hg deposition) µg m-2 yr <.0001 ln (Watershed area) km ln (% forest canopy area) % ln (% wetland area) % <.0001 Population, 2000 Census 1.162E-05 <.0001 ln (slope) unitless: y/x <.0001 ln (mean annual temperature, ) degrees C <.0001 ln (% agricultural land) % weighted watershed alkalinity unitless * <.0001 Interaction term: % shrubland and total Hg deposition <.0001 Interaction term: % forest canopy and total Hg deposition <.0001 Interaction term: Total Hg deposition and watershed alkalinity * <.0001

17 Species-specific predictors Units Coefficient p value loon binary: 1/ <.0001 ln (length) cm <.0001 ln (length): brook trout cm <.0001 ln (length): brown bullhead cm <.0001 ln (length): eastern chain pickerel cm <.0001 ln (length): lake trout cm <.0001 ln (length): land locked salmon cm <.0001 ln (length): largemouth bass cm ln (length): pumpkinseed cm ln (length): white perch cm <.0001 ln (length): white sucker cm <.0001

18 Smallmouth bass (32-cm) predictions

19 Loon Hg predictions

20 Smallmouth bass (32-cm) probability plot

21 Loon Probability Plot

22 Scenario testing 1. Lower Hg deposition to 10.0 μg m -2 yr -1, all sites. 2. Reduce forest cover by 20% 3. Increase mean air temperature 2 C. Compare effect on 32-cm smallmouth bass.

23 Scenario 1 - lower Hg deposition

24 Scenario 2 - reduced forest

25 Scenario 3 - increased air temp.

26 Distribution of predictors Watershed alkalinity Watershed area

27 Implications for Policy Use MERGANSER approach to.. Customize fish consumption advisories. Identify regional or local hot spots. Identify threat to fish-eating wildlife (natural resource management). Assess relative impact of Hg deposition vs. land use practices. Assess net impact of future anthropogenic or climate change.

28 MERGANSER next steps Publish research article (Target: ES&T) Present at scientific meetings Develop interactive mapping tool (EPA) User specifies species and length for single lake or region MERGANSER returns Hg value (with confidence limits) or probability of exceeding threshold value Work with local, state, federal agencies to adopt Outreach to other groups Further modeling explore topographic metrics

29 Conclusions MERGANSER predicts mercury in fish (12 species) and loon for any New England lake >8 ha. Empirical approach uses only parameters with complete spatial coverages. User specifies species and length. MERGANSER returns fish or loon Hg value, or probability of exceeding threshold Model r 2 = 0.63 However, error is relatively high (>50%) Useful as management, policy, and educational tool