Using Relative Risk to Compare the Effects of Aquatic Stressors at a Regional Scale

The regional-scale importance of an aquatic stressor depends both on its regional extent (i.e., how widespread it is) and on the severity of its effects in ecosystems where it is found. Sample surveys, such as those developed by the U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program (EMAP), are designed to estimate and compare the extents, throughout a large region, of elevated conditions for various aquatic stressors. In this article, we propose relative risk as a complementary measure of the severity of each stressor’s effect on a response variable that characterizes aquatic ecological condition. Specifically, relative risk measures the strength of association between stressor and response variables that can be classified as either “good” (i.e., reference) or “poor” (i.e., different from reference). We present formulae for estimating relative risk and its confidence interval, adapted for the unequal sample inclusion probabilities employed in EMAP surveys. For a recent EMAP survey of streams in five Mid-Atlantic states, we estimated the relative extents of eight stressors as well as their relative risks to aquatic macroinvertebrate assemblages, with assemblage condition measured by an index of biotic integrity (IBI). For example, a measure of excess sedimentation had a relative risk of 1.60 for macroinvertebrate IBI, with the meaning that poor IBI conditions were 1.6 times more likely to be found in streams having poor conditions of sedimentation than in streams having good sedimentation conditions. We show how stressor extent and relative risk estimates, viewed together, offer a compact and comprehensive assessment of the relative importances of multiple stressors.     

Model-based combination of spatial information for stream networks

Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial support is of vital importance to environmental science. In this paper we develop a hierarchical spatial statistical model for environmental indicators of stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic units and remote sensing information. These models are used to estimate the indicators throughout the riverine system based on information from multiple sources and aggregate scales. The analysis is based on information underlying the Landscape Atlas of the mid-Atlantic region produced by the US Environmental Monitoring and Assessment Program (EMAP). We also combine information from two overlapping separate monitoring surveys, the EMAP Stream and River Survey and the Maryland Biological Streams Survey. We present a general framework for comparative distributional analysis based on the concept of a relative spatial distribution. As an application, the spatial model is used to predict spatial distributions and relative spatial distributions for a watershed.     

Variance estimation in the EMAP strategy for sampling discrete ecological resources

The United States Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) is designed to describe status, trends and spatial pattern of indicators of condition of the nation's ecological resources. The proposed sampling design for EMAP is based on a triangular systematic grid and employs both variable probability and double sampling. The Horvitz-Thompson estimator provides the foundation of the design-based estimation strategy used in EMAP. However, special features of EMAP designed to accommodate the complexity of sampling environmental resources on a national scale require modifications of standard variance estimation procedures as well as development of new techniques. An overview of variance estimation methods proposed for application to EMAP's sampling strategy for discrete resources is presented.     

Methods development and use of macroinvertebrates as indicators of ecological conditions for streams in the Mid-Atlantic Highlands Region

The Mid-Atlantic Highlands Assessment (MAHA) included the sampling of macroinvertebrates from 424 wadeable stream sites to determine status and trends, biological conditions, and water quality in first through third order streams in the Mid-Atlantic Highlands Region (MAHR) of the United States in 1993–1995. We identified reference and impaired sites using water chemistry and habitat criteria and evaluated a set of candidate macroinvertebrate metrics using a stepwise process. This process examined several metric characteristics, including ability of metrics to discriminate reference and impaired sites, relative scope of impairment, correlations with chemical and habitat indicators of stream disturbance, redundancy with other metrics, and within-year variability. Metrics that performed well were compared with metrics currently being used by three states in the region: Pennsylvania, Virginia, and West Virginia. Some of the metrics used by these states did not perform well when evaluated using regional data, while other metrics used by all three states in some form, specifically number of taxa, number of EPT taxa, and Hilsenhoff Biotic Index, performed well overall. Reasons for discrepancies between state and regional evaluations of metrics are explored. We also provide a set of metrics that, when used in combination, may provide a useful assessment of stream conditions in the MAHR.     

Multivariate Analysis of the Ecoregion Delineation for Aquatic Systems

The ecoregion concept is a popular method of understanding the spatial distribution of the environment', however, it has yet to be adequately demonstrated that the environment is distributed in accordance with these bounded units. In this paper, we generated a testable hypothesis based on the current usage of ecoregions: the ecoregion classification will allow for discrimination between lakes of different water quality. The ecoregion classification should also be more effective better than a comparably scaled classification based on political boundaries, land-use class, or random grouping. To test this hypothesis we used the Environmental Monitoring and Assessment Program (EMAP) lake water chemistry data from the northeast United States. The water chemistry data were reduced to four components using principal component analysis. For comparison to an optimal grouping of these data we used K-means cluster analysis to define the extent at which these lakes could be segregated into distinct classes. Jackknifed discriminant analysis was used to determine the classification rate of ecoregions, the three alternative spatial classification methods, and the clustering algorithm. The classification based on ecoregions was successful for 35% of the lakes included in this study, in comparison to the clustered groups accuracy of 98%. These results suggest that the large scale spatial distribution of ecosystem types is more complicated than that suggested by the present ecoregion boundaries. Further tests of ecoregion delineations are needed and alternative large-scale management strategies should be investigated.     

A DECISION SUPPORT SYSTEM FOR WATER QUALITY DATA AUGMENTATION: A CASE STUDY1

ABSTRACT: Recent developments in water quality monitoring have generated interest in combining non-probability and probability data to improve water quality assessment. The Interagency Task Force on Water Quality Monitoring has taken the lead in exploring data combination possibilities. In this paper we take a developed statistical algorithm for combining the two data types and present an efficient process for implementing the desired data augmentation. In a case study simulated Environmental Protection Agency (EPA) Environmental Monitoring and Assessment Program (EMAP) probability data are combined with auxiliary monitoring station data. Auxiliary stations were identified on the STORET water quality database. The sampling frame is constructed using ARC/INFO and EPA's Reach File-3 (RF3) hydrography data. The procedures for locating auxiliary stations, constructing an EMAP-SWS sampling frame, simulating pollutant exposure, and combining EMAP and auxiliary stations were developed as a decision support system (DSS). In the case study with EMAP, the DSS was used to quantify the expected increases in estimate precision. The benefit of using auxiliary stations in EMAP estimates was measured as the decrease in standard error of the estimate.     

A More Cost-Effective Emap Benthic Macrofaunal Sampling Protocol

Benthic macrofaunal sampling protocols in the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) are to collect 30 to 50 random benthic macrofauna [defined as animals retained on a 0.5 mm (East and Gulf Coasts, USA) or a 1.0 mm mesh sieve (West Coast, USA)] samples per reporting unit using a 0.044 m² (East and Gulf Coasts) or 0.1 m² (West Coast) grab. Benthic macrofaunal community conditions in the reporting unit are characterized by cumulative distribution functions (CDFs) on end points of interest, such as number of species (S), abundance (A), and Shannon--Wiener diversity (H′). An EMAP and a companion field study were conducted concurrently in Tillamook Bay (Oregon, USA) to compare the cost effectiveness of benthic macrofauna samples collected using the EMAP West Coast (0.1 m² × ≥7 cm deep, 1.0 mm mesh), a 0.01 m² × 5 cm deep, 1.0 mm mesh, and a 0.01 m² × 5 cm deep, 0.5 mm mesh sampling protocol. Cost was estimated in relative laboratory sample-processing time. Sampling protocols were judged equally effective for EMAP purposes if, after linear transformation to adjust for scale changes in end point distributions, their S, A, and H′ CDFs were not significantly different. The 0.01 m² × 5 cm deep, 1.0 mm mesh sampling protocol was the most cost effective.     

Correlation of Black Smoke and Nitrogen Oxides Emissions Through Field Testing of in-Use Diesel Vehicles

The U.S. Environmental Protection Agency Environmental Monitoring and Assessment Program (EMAP) uses water clarity as a water quality indicator for integrated assessments of coastal waters. After the publication of the first National Coastal Condition Report (USEPA, 2001c), the national water clarity reference value of 10% of ambient surface light at 1~m depth was reevaluated and modified to reflect expected differences in regional reference light conditions. These regional differences range from naturally turbid estuaries like those found in Mississippi and Louisiana to clear water estuaries expected to support extensive beds of submerged aquatic vegetation in, e.g., Florida and Tampa Bays. For the second National Coastal Condition Report, water clarity was assessed based on regional reference values (USEPA, 2004). Different regional water clarity reference values and data collection methods necessitated the development of a water clarity index based on light attenuation coefficients (k). This index incorporates regional reference conditions and is interchangeable with secchi depth and percent light transmission calculated from light meter measurements. Evaluation of the water clarity index shows that k values based on transmissivity at 1~m can be estimated from secchi depth measurements and successfully used as a surrogate for transmissivity calculated from light meter data. An approach for assessing water clarity in Gulf of Mexico estuaries using light meter data and secchi depth is presented.     

A Regional Assessment of Windbreak Habitat Suitability

The Environmental Monitoring and Assessment Programwas initiated in 1989 by the United StatesEnvironmental Protection Agency to collect, analyze,and report quantitative, statistically unbiasedinformation about the state of the nation'senvironment on a regional basis. During a pilotprogram in Nebraska we measured a habitat suitabilityindex for a probability sample of 40 windbreaks andexpanded the results to estimate the potential valueof windbreaks as wildlife habitat in Nebraska. Theindex estimates the suitability of a windbreak ashabitat for wildlife including breeding birds, smallmammals, and deer. Index values range from zero toone, where a value of one indicates maximal habitatvalue. We estimated that 50% (±13% at 90%confidence) of windbreaks in Nebraska have a habitatsuitability index of 0.25 or less and that nowindbreaks have a suitability index greater than 0.6. Our results indicate that increasing the area ofindividual windbreaks is the most effective way toimprove their value as wildlife habitat. Monitoringwindbreak condition over time would alert wildlifemanagers to changes in the resource that might affectwildlife populations. Because our data were highlyvariable, the power to detect change in habitatcondition between two measurement periods was low. Amuch larger sample would be required to detect smallchanges in habitat condition. Variability may bereduced, and power increased, by carefully andconsistently constructing the sampling frame, keepingdata collection as simple as possible, appropriatelystratifying sample selection, and using a small numberof well-trained data collection teams. However, wesuggest adapting the index for use with aerialphotography in future efforts to evaluate windbreaksas wildlife habitat in extensive areas.     

Development and evaluation of a Macroinvertebrate Biotic Integrity Index (MBII) for regionally assessing Mid-Atlantic Highlands Streams

The Macroinvertebrate Biotic Integrity Index (MBII) was developed from data collected at 574 wadeable stream reaches in the Mid-Atlantic Highlands region (MAHR) by the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program (EMAP). Over 100 candidate metrics were evaluated for range, precision, responsiveness to various disturbances, relationship to catchment area, and redundancy. Seven metrics were selected, representing taxa richness (Ephemeroptera richness, Plecoptera richness, Trichoptera richness), assemblage composition (percent non-insect individuals, percent 5 dominant taxa), pollution tolerance [Macroinvertebrate Tolerance Index (MTI)], and one functional feeding group (collector-filterer richness). We scored metrics and summed them, then ranked the resulting index through use of independently evaluated reference stream reaches. Although sites were classified into lowland and upland ecoregional groups, we did not need to develop separate scoring criteria for each ecoregional group. We were able to use the same metrics for pool and riffle composite samples, but we had to score them differently. Using the EMAP probability design, we inferred the results, with known confidence bounds, to the 167,797 kilometers of wadeable streams in the Mid-Atlantic Highlands. We classified 17% of the target stream length in the MAHR as good, 57% as fair, and 26% as poor. Pool-dominated reaches were relatively rare in the MAHR, and the usefulness of the MBII was more difficult to assess in these reaches. The process used for developing the MBII is widely applicable and resulted in an index effective in evaluating region-wide conditions and distinguishing good and impaired reaches among both upland and lowland streams dominated by riffle habitat.