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标题：Comparing and Integrating Fish Surveys in the San Francisco Estuary: Why Diverse Long-Term Monitoring Programs are Important
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作者：Stompe, Dylan K. ; Moyle, Peter B. ; Kruger, Avery 等
期刊名称：San Francisco Estuary and Watershed Science
印刷版ISSN：1546-2366
出版年度：2020
卷号：18
期号：2
DOI：10.15447/sfews.2020v18iss2art4
出版社：San Francisco Bay-Delta Science Consortium and the John Muir Institute of the Environment
摘要：Many fishes in the San Francisco Estuary have suffered declines in recent decades, as shown by numerous long-term monitoring programs. A long-term monitoring program, such as the Interagency Ecological Program, comprises a suite of surveys, each conducted by a state or federal agency or academic institution. These types of programs have produced rich data sets that are useful for tracking species trends over time. Problems arise from drawing conclusions based on one or few surveys because each survey samples a different subset of species or reflects different spatial or temporal trends in abundance. The challenges in using data sets from these surveys for comparative purposes stem from methodological differences, magnitude of data, incompatible data formats, and end-user preference for familiar surveys. To improve the utility of these data sets and encourage multi-survey analyses, we quantitatively rate these surveys based on their ability to represent species trends, present a methodology for integrating long-term data sets, and provide examples that highlight the importance of expanded analyses. We identify areas and species that are under-sampled, and compare fish salvage data from large water export facilities with survey data. Our analysis indicates that while surveys are redundant for some species, no two surveys are completely duplicative. Differing trends become evident when considering individual and aggregate survey data, because they imply spatial, seasonal, or gear-dependent catch. Our quantitative ratings and integrated data set allow for improved and better-informed comparisons of species trends across surveys, while highlighting the importance of the current array of sampling methodologies.