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IMPROVING THE COLLECTION, MANAGEMENT, AND USE OF MARINE FISHERIES DATA
million on vessel monitoring system (VMS) programs. The expenditure by NMFS for these data collection activities is thus on the order of $45 million. Additional expenditures were made by states and industry. The total fishery harvest in the United States (commercial and recreational) is valued at approximately $45.7 billion when the total economic effects are included (NMFS, 1995).
WHO USES FISHERIES DATA?
Fisheries data have many uses and many users—including stock assessment by scientists, strategic planning by industry, and fishery monitoring and allocation decisions by managers. Adequacy of data can be evaluated only in the context of the purposes for which they are used. Each use implies a set of users and a suite of requirements that the data must satisfy, including timeliness, level of detail, accuracy, accessibility to users, coverage or completeness, and credibility of the data collection process and the management process that uses the data.
Fisheries data are vital to strategic planning activities in coastal communities that rely on fisheries. Fishery management authorities are responsible to use fisheries data for creating policies for the orderly and sustainable development and management of fisheries. Civil authorities use fisheries data to site marinas, underwater pipes and cables, and other maritime facilities, and to develop infrastructure for the fishing industry. Bankers use fisheries data to plan economic development and loan packages to fishermen, fish processors, and ship suppliers. Fishermen themselves use fisheries data to plan future fishing activities, such as shifts to new fishing grounds, changes in fishing gear, and changes in species targeted. However, fishermen often use their own data sources, including their own logbooks and observations, and what they learn from other fishermen and buyers, instead of using government data. This may occur because of some fishermen's mistrust of government data, the frequent lag time in availability of such data (often too great to use government data in business planning), and the lack of data for the geographic area and type of fishery in which a specific fisherman is engaged.
Monitoring conditions in a fishery is the responsibility of regional fishery management councils and NMFS, and is the primary means of assessing compliance with and accomplishing enforcement of fishery regulations. Another major responsibility of the regional councils is allocation of harvest opportunities among different user groups. Environmental and other interest groups also have become increasingly involved in monitoring fishing activities. Monitoring often requires data with great detail in both time and space as well as frequent updates, often within a fishing season.
Stock assessment is a critical use of fisheries data and is often considered its primary use. The committee devoted a significant portion of its attention to the data used in stock assessments, using the summer flounder fishery as a case study. Scientists employed by state, interstate, national, and international fishery agencies are the primary users of data relevant to stock assessments; in addition, university and private sector scientists increasingly are becoming involved in stock assessments and related research. Current stock assessment practices use data aggregated over the entire fishing ground and over a fishing season. Although assessment methods may require a greater diversity of data, the resolution in space and time is usually rather coarse and may need updating only infrequently, such as annually or semi-annually.
The multiple users of fisheries data have different requirements in terms of resolution in time and space for each possible data element. Table 3-1 summarizes the requirements for data elements by various users (based on committee experience); specific details depend on the characteristics of individual fisheries. Data system designers, therefore, must consider that the demands will vary among users, and the system must be capable of accommodating users who require data at different spatial resolutions and different degrees of timeliness.