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A double-integration hypothesis to explain ocean ecosystem response to climate forcing

Long-term time series of marine ecological indicators often are characterized by large-amplitude state transitions that can persist for decades. Understanding the significance of these variations depends critically on the underlying hypotheses characterizing expected natural variability. Using a linear autoregressive model in combination with long-term zooplankton observations off the California coast, we show that cumulative integrations of white-noise atmospheric forcing can generate marine population responses that are characterized by strong transitions and prolonged apparent state changes. This model provides a baseline hypothesis for explaining ecosystem variability and for interpreting the significance of abrupt responses and climate change signatures in marine ecosystems.
- University of California, San Diego United States
- University of California System United States
- Scripps Institution of Oceanography United States
- Georgia Institute of Technology United States
- Georgia Institute of Technology United States
climate variability, Time Factors, zooplankton dynamics, Climate Change, Oceans and Seas, Population Dynamics, Biological, Models, Biological, California Current, California, Zooplankton, Climate Action, ocean climate, Models, ecosystem shifts, Linear Models, Animals, Life Below Water, Ecosystem
climate variability, Time Factors, zooplankton dynamics, Climate Change, Oceans and Seas, Population Dynamics, Biological, Models, Biological, California Current, California, Zooplankton, Climate Action, ocean climate, Models, ecosystem shifts, Linear Models, Animals, Life Below Water, Ecosystem
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).114 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
