| Summary: | Long-term data are crucial for understanding ecological responses to climate and land use change; they are also vital evidence for informing management. As a migratory fish, Atlantic salmon are sentinels of both global and local environmental change. This paper reviews the main insights from six decades of research in an upland Scottish stream (Girnock Burn) inhabited by a spring Atlantic salmon population dominated by multi-sea-winter fish. Research began in the 1960s providing a census of returning adults, juvenile emigrants and in-stream production of Atlantic salmon. Early research pioneered new monitoring techniques providing new insights into salmon ecology and population dynamics. These studies underlined the need for interdisciplinary approaches for understanding salmon interactions with physical, chemical and biological components of in-stream habitats at different life-stages. This highlighted variations in catchment-scale hydroclimate, hydrology, geomorphology and hydrochemistry as essential to understanding freshwater habitats in the wider landscape context. Evolution of research has resulted in a remarkable catalogue of novel findings underlining the value of long-term data that increases with time as modelling tools advance to leverage more insights from “big data”. Data are available on fish numbers, sizes and ages across multiple life stages, extending over many decades and covering a wide range of stock levels. Combined with an unusually detailed characterisation of the environment, these data have enabled a unique process-based understanding of the controls and bottlenecks on salmon population dynamics across the entire lifecycle and the consequences of declining marine survival and ova deposition. Such powerful datasets, methodological enhancements and the resulting process understanding have informed and supported the development of fish population assessment tools which have been applied to aid management of threatened salmon stocks at large-catchment, regional and national scales. Many ...
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