Comparison of Variance Estimators of the Horvitz-Thompson Estimator for Randomized Variable Probability Systematic Sampling

Most Great River ecosystems (GREs) are extensively modified and are not receiving adequate protection to prevent further habitat degradation and loss of biotic integrity. In the United States, ecological monitoring and assessment of GREs has lagged behind streams and estuaries, and the management of GREs is hampered by the lack of unbiased data at appropriate spatial scales. Properties of GREs that make them challenging to monitor and assess include difficult sample logistics and high habitat diversity. The U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) has developed a comprehensive, regional-scale, survey-based monitoring approach to assessment of streams and estuaries, but has not yet conducted research on applying these tools to GRE monitoring. In this paper we present an overview of an EMAP research project on the Upper Missouri River (UMR). We summarize the assessment objectives for the study, the design for selecting sample locations, the indicators measured at these sites and the tools used to analyze data. We present an example of the type of statements that can be made with EMAP monitoring data. With modification, the set of methodologies developed by EMAP may be well suited for assessment of GREs in general.

The Environmental Monitoring and Assessment Program (EMAP) of the U.S. Environmental Protection Agency has conducted several probability surveys of aquatic resources. Such surveys usually have unequal probability of including population elements in the sample. The Northeast lakes survey, which motivated this study of variance estimation, was such a survey. We examine ten estimators for the finite population variance using a Monte Carlo factorial experiment that considers three population characteristics. The results show that the correlation between the inclusion probabilities and the response is the most important factor that differentiates the estimators. Under conditions of low correlation (approximately <0.4), a common feature in environmental surveys, the sample variance is best, elsewhere, two ratio estimators, one based on consistency and the Horvitz-Thompson Theorem (HT) and the other based on the Yates-Grundy form, behave similarly and best.

The U.S. Environmental Protection Agency (EPA) initiated the Environmental Monitoring and Assessment Program (EMAP) in 1988. The wetland component (EMAP-Wetlands) is designed to provide quantitative assessments of the current status and long-term trends in the ecological condition of wetland resources. EMAP-Wetlands will develop a wetland monitoring network and will identify and evaluate indicators that describe and quantify wetland condition. The EMAP-Wetlands network will represent a probability sample of the total wetland resource. The EMAP sample is based on a triangular grid of approximately 12,600 sample points in the conterminous U.S. The triangular grid adequately samples wetland resources that are common and uniformly distributed in a region, such as the prairie pothole wetlands of the Midwest. However, the design is flexible and allows the base grid density to be increased to adequately sample wetland resources, such as the coastal wetlands of the Gulf of Mexico, which are distributed linearly along the coast. The Gulf sample network required a 49-fold increase in base grid density. EMAP-Wetlands aggregates the 56 U.S. Fish and Wildlife Service’s (FWS) National Wetland Inventory (NWI) categories (Cowardin et al. 1979) into 12 functionally similar groups (Leibowitz et al. 1991). Both the EMAP sample design and aggregated wetland classes are suitable for global inventory and assessment of wetlands.

Beginning in 1995, the U.S. Environmental Protection Agency (U.S. EPA), Office of Research and Development has focused much of its ecological research in the Mid-Atlantic as part of the Mid-Atlantic Integrated Assessment (MAIA). The goal of MAIA is to improve the assessability of scientific information in environmental decision-making. Following the Environmental Monitoring and Assessment Program (EMAP) whose goal is to guide monitoring that effectively reflects current ecosystem condition and trends, MAIA's second, current, phase of research under the Regional Vulnerability Assessment (ReVA) program is designed to target risk management activities using available data and models. The papers presented here are from a conference held in May 2003 that presented results of research in this second phase of MAIA. The conference was organized into the following topics: 1. Assessing Current Impacts and Vulnerabilities 2. Forecasting Environmental Condition and Vulnerabilities 3. Developing Management Strategies to Optimize the Future, and 4. Assessing and Responding to Environmental Vulnerability.

The Committee on Environment and Natural Resources (CENR) has recognized a high priority need to integrate and coordinate federal agencies' efforts in order to enable a comprehensive evaluation of our nation's environmental resources and ecological systems. The federal government spends about $640 million per year collecting data about our forests, agricultural and rangelands, lakes, rivers, estuaries, and coastal marine systems. These efforts have significantly aided the progress in preserving and protecting the environment in recent decades but are not sufficiently coordinated to provide us a truly comprehensive status report or full understanding of the causes and effects of environmental change. This paper describes the Committee on Environment and Natural Resources and its functions, provides a status report on the Environmental Monitoring and Research Initiative, and offers some perspectives on the factors that will make the initiative and its contributing programs a success. In particular, the paper discusses the potential relationship with the Environmental Monitoring and Assessment Program (EMAP).

Section 305(b) of the United States' Clean Water Act (CWA) requires states to assess the overall quality of waters in the states, while Section 303(d) requires states to develop a list of the specific waters in their state not attaining water quality standards (a.k.a impaired waters). An integrated, efficient and cost-effective process is needed to acquire and assess the data needed to meet both these mandates. A subset of presentations at the 2002 Environmental Monitoring and Assessment Program (EMAP) Symposium provided information on how probability data, tools and methods could be used by states and other entities to aid in development of their overall assessment of condition and list of impaired waters. Discussion identified some of the technical and institutional problems that hinder the use of EMAP methods and data in the analysis to identify impaired waters as well as development needs to overcome these problems.

Developing and applying a benthic index of estuarine condition for the Virginian Biogeographic Province

Structural physical habitat attributes include indices of stream size, channel gradient, substrate size, habitat complexity, and riparian vegetation cover and structure. The Environmental Monitoring and Assessment Program (EMAP) is designed to assess the status and trends of ecological resources at different scales. High-resolution remote sensing provides unique capabilities in detecting a variety of features and indicators of environmental health and condition. LIDAR is an airborne scanning laser system that provides data on topography, channel dimensions (width, depth), slope, channel complexity (residual pools, volume, morphometric complexity, hydraulic roughness), riparian vegetation (height and density), dimensions of riparian zone, anthropogenic alterations and disturbances, and channel and riparian interaction. Hyperspectral aerial imagery offers the advantage of high spectral and spatial resolution allowing for the detection and identification of riparian vegetation and natural and anthropogenic features at a resolution not possible with satellite imagery. When combined, or fused, these technologies comprise a powerful geospatial data set for assessing and monitoring lentic and lotic environmental characteristics and condition.

One of the dilemmas in designing any large-scale macroinvertebrate bioassessment is deciding where to sample within streams. Streams contain a wide variety of habitats with varying macroinvertebrate assemblages, yet consistency in sampling protocol is needed to interpret results across sites in a region. The Environmental Monitoring and Assessment Program (EMAP) conducted large regional probability surveys in the mid-Atlantic (1993–1998) and the western US (2000–2001). In these surveys, 2 macroinvertebrate sample types were collected at each site: pool and riffle in the mid-Atlantic region, and reachwide and riffle in the western US. We analyzed data from sites where both types of samples had been collected (206 mid-Atlantic and 293 western US sites) to examine the effects of sample type on typical metric and multivariate analyses done in bioassessments. Sample types differed in terms of taxon richness measures and assemblage composition, and differences were more pronounced between mid-Atlantic riffle and pool samples than between western US reachwide and riffle samples. Nonetheless, sample-type differences did not obscure the overall pattern in ordination analyses, nor did they influence detection of important environmental gradients. In addition, bioassessments based on Ephemeroptera, Plecoptera, and Trichoptera taxon richness showed that regional assessments differed little with sample type. Our analyses indicate that typical bioassessment methods are relatively robust with respect to sample type in regional surveys. Reachwide sampling could be used with little risk to the quality of assessments in surveys covering large geographic areas where a single targeted habitat may not occur at all sites. Reachwide sampling is also easy to apply consistently at most sites and requires approximately the same effort and cost as single-habitat sampling.

Many lakes in the Adirondack Mountains, New York, have acidified over the past century due to acidic atmospheric deposition. More recently, most monitored lakes have shown signs of chemical recovery (increase in acid neutralizing capacity) as sulfur deposition levels have declined in response to the Clean Air Act and other emissions control legislation. We used measured and modeled trends in past lakewater acidification and projections of future recovery from acidification to extrapolate results from judgment samples of intensively studied lakes to the population of acid-sensitive Adirondack lakes. Simulations were developed for 70 watersheds using the Model of Acidification of Groundwater in Catchments (MAGIC) to classify lakes according to their sensitivity to change in atmospheric S and N deposition. MAGIC simulations suggested that the modeled Adirondack Long-Term Monitoring Project (ALTM) and Adirondack Effects Assessment Project (AEAP) lakes were largely among the lakes in the population that had acidified most between 1850 and 1990. Most of the modeled ALTM/AEAP lakes were within the top 36% of acid sensitivity, based on model projections of past acidification and future chemical recovery, compared with the 1,829 Adirondack lakes in EPA’s Environmental Monitoring and Assessment Program (EMAP) statistical frame. Results of this research will allow fuller utilization of data from on-going chemical and biological monitoring and process-level studies by providing a basis for regionalization of findings and developing/refining relationships among watershed characteristics, chemical change, and biological responses to changing levels of acidic deposition.

A study to demonstrate the use of remotely sensed data to identify vegetation change was conducted in the Upper San Pedro River watershed in southeastern Arizona. This project was coordinated and funded by the Environmental Protection Agency (EPA) Environmental Monitoring and Assessment Program (EMAP) Arid ecosystems as a pilot study with three specific objectives: to evaluate the characterization of land cover using remote sensing systems, to determine the applicability of the Brown, Lowe and Pase (1979) vegetation classification system to such a land characterization system, and to apply techniques for detecting change over time in land cover from remotely sensed data. Whilst possible causes of vegetation change are discussed, the pilot study was not designed to determine the causes of any changes that were identified. The study area was chosen because of its characteristic physiography and vegetation and because of the rapid land cover changes known to have taken place. In addition, interagency data, remote sensing coverage and historical documentation covering a period of 52 years were available for the area. Notes Current address: U.S. Environmental Protection Agency, Environmental Research Laboratory, 200 SW 35th Street, Corvallis, OR 97333, U.S.A.

Standard protocols are critical for maximizing data comparability and aggregation in national monitoring programs, and taxa richness is a common indicator of site condition and biological diversity. There are two general approaches for sampling stream macroinvertebrate assemblages: targeted richest habitat and site wide. At seven sites, we compared three methods: Ontario Benthic Biomonitoring Network (OBBN), Environmental Monitoring and Assessment Program (EMAP), and Rapid Bioassessment Protocol (RBP). The OBBN method produced a biased sample at a site with a single small riffle, the RBP method produced the most total taxa, and the EMAP method produced the most taxa at four sites and the most individuals at six sites. The RBP method produced asymptotes for percent tolerant individuals, percent chironomid individuals, and Hilsenhoff Biotic Index score after five to ten stations. The EMAP method produced asymptotes for those metrics after 10 to 20 stations per site. The EMAP method typically required half the number of stations as the RBP method to obtain 70-90% of true taxa richness as estimated by the Jaccard coefficient. We conclude that the EMAP method is preferable because of its greater precision in taxa richness estimates.

When the U.S. Congress passed the Federal Water Pollution Control Act in 1972, later amended by the Clean Water Act (CWA) of 1977, it tasked the newly created U.S. Environmental Protection Agency (EPA), in cooperation with the states, with periodically assessing the quality of U.S. waters and reporting on progress toward meeting the goals of the CWA. In subsequent decades, reviews by various governmental and nongovernmental organizations consistently have found available water quality data and reporting to be inadequate to evaluate the nation's progress [Shapiro et al., 2008]. In response to these concerns, in 1989 EPA's Office of Research and Development initiated the Environmental Monitoring and Assessment Program (EMAP) to develop and demonstrate scientific tools to monitor the status of, and trends in, U.S. aquatic resources and environmental stressors affecting them.Recent EPA‐led efforts involve monitoring wadeable perennial streams (streams or rivers shallow enough to be wadeable during seasonal low flows), which make up an estimated 90% of the total length of all perennial flowing waters in the United States [EPA, 2006]. Selected results from the first national survey of these streams, the national Wadeable Streams Assessment (WSA) [EPA, 2006; Paulsen et al., 2008], illustrate how such surveys can provide critical information to guide management of this important resource. Nonmonitoring applications of data from the WSA and earlier regional surveys show the wide‐ranging applicability of these rich data sets.

The rapid spread of the exotic zebra mussel (Dreissena polymorpha) concerns aquatic resource managers in the United States and Canada. Since 1990, it has been spreading from the Great Lakes into the Northeast. The zebra mussel cannot colonize equally in northeastern lakes in part because soft water causes ion exchange and reproductive problems when [Ca2+] reaches a lower limit of approximately 12 mg/L, and pH drops below 7.3. We used (1) an alkalinity map to conservatively delineate areas expected to be at low risk for zebra mussel invasion and (2) water chemistry data from 344 lakes sampled in the 1991–1994 Environmental Monitoring and Assessment Program (EMAP) to estimate numbers and surface area of lakes at risk in the Northeast (New England, New York, and New Jersey). These lakes were randomly selected from a target population of 11,080 lakes > 1 and < 10,000 ha using a probability design that allowed inference to all lakes in the region with known confidence. An estimated 25% (2,770 SE = 410...

Regional vulnerability assessment, or ReVA, is an approach to place‐based ecological risk assessment that is currently under development by the Office of Research and Development of the U.S. Environmental Protection Agency (EPA). The assessment is done at the scale of EPA regions and builds on data collected for the Environmental Monitoring and Assessment Program (EMAP) of the EPA. The pilot ReVA is being developed for the U.S. mid‐Atlantic region to identify those ecosystems, together with the ecological goods and services they provide, that are most vulnerable to being lost in the next 20 years. The project is currently exploring different conceptual approaches to integrated assessment. In this article, we give an operational approach to estimating ecosystem vulnerability and discuss important issues arising from it. The first issue is estimating vulnerability at the regional scale as opposed to the more familiar local scale. The second issue is integrating information about different sorts of risks in order to prioritize them at the regional scale. The challenge of integration is considerable because of the possibility of synergistic (mutually reinforcing) interactions between different environmental stresses. Synergistic effects are often too poorly known to include, yet potentially too important to ignore. Vulnerability at the regional scale may provide a pragmatic, middle‐road approach to this problem by highlighting and characterizing geographic areas that are expected to change the most in the future. The goal is not exact predictions, but a first‐cut early warning system to identify and prioritize the risks of undesirable environmental changes over the next few decades.