ORD’s Environmental Monitoring and Assessment Program (EMAP) Sound Science for Measuring Ecological Condition

Key EPA Monitoring Questions What are the current conditions of our ecosystems? Where are the conditions improving or declining? What stresses are associated with declines? Are our management programs and policies working?

What’s at Stake? >$1B/y spent on monitoring Condition of estuaries, coastlines, streams, rivers, wetlands and lakes are still unknown. Effectiveness of protection and restoration programs and policies are often unknown

GOALS of EMAP Develop the scientific basis for consistent, unbiased, cost-effective measurement of the condition of the Nation’s aquatic ecosystems –Status –Trends Build state and tribal capacity for monitoring condition and transfer our technology Make data generally available to all stakeholders (STORET)

No additional Sampling (continue to Monitor as part of 5-year cycle) Condition Waterbody has high Probability of Impairment 305(b) Reports Waterbody Impairment Confirmed 303(d) List TMDL Development States conduct Probability survey With suite of indicators Remediation Intensive sampling to confirm impairment State of the Environment Reports Associated Stressors Point Source Non-point Source Likelihood Criteria Dose - Response Comparison of # of Expected 303(d) Sites to known sites = Accept State 303(d) list Probability of Impairment Assessment Models Waterbody has low Probability of Impairment De-list Waterbody Not impaired Diagnosis Integrated Monitoring Waterbody has Moderate Probability of impairment Standards

Science Behind the Scenes Population Identification Variable Density Approaches Frame Development Spatial Balance Panel Rotation Variance Estimation Status (Trends) Index Construction Index Calibration Index Regionalization Reference Condition Ecoregion Framework Indicators Designs = Assessments Partnerships Analysis Data Field Sampling Training +

CWA: Resource Monitoring Needs National Implementation National Demonstration Regional Demonstration R-EMAP/Small Scale Tests Implementation Land cover/use Indicators Design Classification Strata Reference Conditions Nationally Consistent Design Science Barriers Coastal Streams Large Rivers Great Lakes Lakes Wetlands Resource Areas

EMAP Extramural Research Areas Coastal Initiative – 60% to State Co-ops Western Pilot – 60% to State Co-ops GRI – 60% to State and other Co-ops R-EMAP – 100% to EPA Regions STAR – 100% to Academic Research Institutions STAR Grants Western Pilot R-EMAP Coastal Initiative GRI

EMAP Design Approach Probabilistic Design Framework – Randomized statistical designs that allow interpretation of monitoring data with known uncertainty, extrapolation to the entire population of interest with a small sample size, and the ability to statistically aggregate similar data to larger geographic areas Classification - meaningful groupings within resource types and/or ecosystem types to allow better statistical design and analysis Biological Indicators - Direct measures of aquatic ecosystem condition, integrates stressors, and the public can relate to them –Streams, rivers, estuaries, lakes, reservoirs, wetlands

Probabilistic Survey Design Advantages Representative and allows inference to system of interest Adaptable to resource characteristics Adjusts sample sizes to meet precision requirements Adaptable to temporal and spatial scales of interest Unbiased Cost-effective Fully Supporting 87% Not Supporting 13% Traditional Targeted Monitoring Fully Supporting 13% Not Supporting 87% Probability Survey Fully Supporting 95% Not Support ing 5% Fully Supporting 75% Not Supporting 25% Delaware Nebraska Traditional Targeted Monitoring Probability Survey Condition of streams

EMAP Uses Biological Indicators Historic Aquatic Indicators – Measured physical/chemical characteristics and related them to the biological condition of an aquatic system Aquatic Biological Indicators – Direct measure of condition of aquatic ecosystem, integrates stressors, and the public can relate

Eutrophication of NE US lakes – 4219 mostly problem lakes sampled by states for 305(b) – 2756 non-random lakes censused (Rohm et al. 1995) – 344 lakes with EMAP probability design (11,076 lakes total) Alabama reduced the cost of estuarine monitoring by ~33%, and can now report on all estuarine waters Effectiveness of Design % Impaired Lakes Sampling costs

Fish IBI Proportion of Stream Length (Insufficient Data) Good Fair Poor % of Stream Length 0%10%20%30% 40% Riparian Habitat Sedimentation Mine Drainage Acidic Deposition Tissue Contamination Phosphorus Acid Mine Drainage 24% 25% 14% 11% 10% 5% 1% Nitrogen 5% Potential Stressors 0%10%20%30%40% Introduced Fish 34% Stream Conditions in MAHA

Degraded 30 ± 6% Undegraded 70 ± 6% Degraded 18 ± 8% Undegraded 82 ± 8% Louisianian ProvinceVirginian Province Metals 42% Toxicity 4% Contaminants 28% Low D.O. Habitat 14% Unknown 10% Unknown 39% Contaminants 10% Both 2% Low Dissolved Oxygen 49% Condition Stressors Associated with Degraded Condition Estuarine Conditions

% area with impaired benthos Statistical Change Detection Change in Percent Area of Chesapeake Bay with Impaired Benthic Community *

EMAP National Demonstrations Estuaries – All 24 marine coastal states monitoring with core EMAP design and indicators Streams – Mid-Atlantic States and 12 Western States Great Rivers – Mississippi River Basin

No additional Sampling (continue to Monitor as part of 5-year cycle) Condition Waterbody has high Probability of Impairment 305(b) Reports Waterbody Impairment Confirmed 303(d) List TMDL Development States conduct Probability survey With suite of indicators Remediation Intensive sampling to confirm impairment State of the Environment Reports Associated Stressors Point Source Non-point Source Thresholds of Impairment Dose - Response Comparison of # of Expected 303(d) Sites to known sites = Accept State 303(d) list Probability of Impairment Assessment Models (2 levels) Waterbody has low Probability of Impairment De-list Waterbody Not impaired Diagnosis Integrated Monitoring and Assessment Waterbody has Moderate Probability of impairment Standards ,7 9,10 10

Example of Integrated Monitoring and Assessment with Maryland Biological Stream Survey Data MBSS probability survey for benthic IBI and fish IBI measures of stream condition (impairment for BIBI < 3, FIBI < 3), chemical and physical measurements taken, land cover data available Analysis: cumulative distribution functions (cdfs) conditional probabilities conditional cdfs 1

Condition of Streams in Maryland 54% of 1st order stream miles are impaired (BIBI < 3) 40% of 2nd order stream miles are impaired (BIBI < 3) 47% of 1st order stream miles are impaired (FIBI < 3) 24% of 2nd order stream miles are impaired (FIBI < 3) 2

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Associated Stressors 5

MBSS-derived thresholds of impairment: pH < 5 ANC < 200 μeq/l Nitrate-nitrogen > 2 mg/l DO < 5 ppm Sulfate > 24 mg/l DOC > 8.0 ppm Conditional probability thresholds of impairment: 1st order steams: DO 12 pH 8 NO3 < 5, <15 SO4 < Temp 25 Hilsenoff 6 2nd order steams: DO 11 pH 8.5 NO3 < ? SO4 < 75 Temp 28 Hilsenoff <2, Thresholds of Impairment 6 7

Percent Fines in Substrate British Columbia Conditional value 7

8800 stream miles stream miles in MD 66% 1 st order % 2 nd order Impaired Streams in Maryland 7304 miles in 1 st and 2 nd order streams 3725 miles of 1 st and 2 nd order streams should be on 303(d) List based on benthic impairment 8

Agriculture on >3% Slopes Probability of Impairment Models Combine condition information with land cover data to predict probability of impairment 9 Data to Drive Modeling Spatial Models for Probability of Impairment 10

Probability of Stream Benthic Impairment for Exceeding Levels of Catchment Urbanization 10