摘要
Freshwater biological monitoring and assessment programs using biological indicators of ecological integrity (biocriteria) are integral to successful water resources planning and decision making. In the United States, the Clean Water Act requires every state to evaluate whether or not the designated aquatic life use (defined in its water quality standards) is being attained for each river and stream, and to submit a biennial list of impaired waters for US Environmental Protection Agency (EPA) approval. Economic constraints (personnel, equipment, transportation, laboratory, and data management costs) on water quality monitoring present a considerable challenge to states in reaching this goal. Many US states are using biocriteria-based statewide monitoring of the condition of surface waters to meet this challenge. The sampling effort of state monitoring programs, however, is often not sufficient to provide reliable estimates of stream condition for individual watersheds. Fortunately, other organizations such as county and municipal governments, regional water management authorities, and volunteer watershed groups are also gathering valuable stream monitoring data. When more than one monitoring program is conducted in a local watershed, it is desirable to integrate the estimates to (1) provide consistent estimates of stream condition, (2) increase the effective sample size and hence precision of the estimates, and (3) improve the spatial coverage of the stream network. In this paper, we show how a composite estimator can be used to combine the results of more than one probability-based survey to estimate mean condition in watersheds. As an example, we estimate the mean Fish Index of Biotic Integrity (IBI) for the Seneca Creek watershed in the State of Maryland, combining estimates from the Maryland Biological Stream Survey (MBSS) and the Montgomery County Stream Monitoring Program. Separate estimates are provided for the network of streams that are in the sample frame of both surveys and for the expanded stream network covered only by the Montgomery County survey. The composite estimate of mean Fish IBI for the streams common to both programs has a lower standard error than the MBSS estimate and yields a consistent estimate of stream condition that can be used to evaluate Seneca Creek watershed for inclusion in the State’s priority list of impaired waters. For the expanded stream network, the composite estimation also increased the precision of the mean Fish IBI relative to the Montgomery County estimate. The combination of surveys across space result in increased precision because of increased sample sizes and the application of weights that minimize the variance, and hence can provide more definitive classification of water bodies and reduce the need for follow-up monitoring. Integration of survey estimates can improve communication with the public and ultimately lead to more reliable water resource management decisions. State, provincial, and regional authorities in the US and other countries can use the composite indicator estimation technique presented here to derive more information from their limited monitoring resources and make better water resource protection decisions.