Biological Time Series

Datasets spanning multiple years are some of the most powerful tools that scientists have to describe and understand ecosystem function, variation and resilience. Yet, these long-term datasets are rare. The few long-term datasets that exist are typically limited to either physical or biological data, and are usually very limited in their geographic scope. PISCO’s long-term program of monitoring and experimental research provides this information at the latitudinal scale of the California Current Large Marine Ecosystem (CCLME). The approach allows scientists to identify and understand ecological patterns and how those patterns are affected by large-scale ocean conditions. To-date, PISCO has made substantial progress toward understanding changes over time and ecological interactions in the species rich and highly productive kelp forests and rocky shores along the coastal ocean and nearshore habitats of the CCLME.

Consistent long-term monitoring at the geographic scale has produced valuable datasets and the large scale, long-term context that can be used to understand potential drivers of patterns and environmental changes. These data and results are relevant for managing marine resources, understanding climate change impacts, and elucidating the role of marine reserves as tools for ocean resource management. Some recent examples of major findings include assessing the impact of El Niño events on population replenishment of rockfish, revealing previously unknown patterns of biodiversity along the west coast, and documentation and understanding of a seasonal hypoxia zone located off the Oregon and Washington coasts. These and other emerging results demonstrate the high value of these long-term ecological data series for purposes of understanding and predicting how species and ecosystems will respond in the face of changing ocean circulation patterns, acidification, warming, altered patterns of fishing, and other environmental perturbations.

The value of this large scale, long-term approach builds through time, particularly as we begin to discover how biological communities are affected by long-term physical drivers such as El Nino events and Pacific Decadal Oscillations. These datasets are also important in the short term for specific management and policy applications. In recent years, results have been integral for endangered species management, oil spill response, assessments of erosion events, the monitoring and evaluation of marine reserves and fisheries management issues.

 

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