Evaluating ecosystem change as Gulf of Alaska temperature exceeds the limits of preindustrial variability. (July 2020)

• Record Type: Journal Article
• Authors: Litzow, Michael A.; Hunsicker, Mary E.; Ward, Eric J.; Anderson, Sean C.; Gao, Jin; Zador, Stephani G.; Batten, Sonia; Dressel, Sherri C.; Duffy-Anderson, Janet; Fergusson, Emily; Hopcroft, Russell R.; Laurel, Benjamin J.; O'Malley, Robert
• Is Part Of: Progress in oceanography. Volume 186(2020)
• Publication Date: 2020-07
• Subjects: Bayesian Dynamic Factor Analysis -- Climate change -- Climate variability -- Gulf of Alaska -- Ecosystem response -- Nonstationary relationship; Oceanography -- Periodicals; 551.4605
• DOI: 10.1016/j.pocean.2020.102393
• Languages: English
• ISSNs: 0079-6611

Abstract

Highlights: We evaluated the community-level impacts of extreme Gulf of Alaska temperatures. Plankton community response to temperature indicates bottom-up climate effects. Upper trophic level responses to temperature have changed over time. Correlations among climate and community variables have also changed over time. We found no evidence of an incipient shift to an alternative community state. Abstract: The Gulf of Alaska experienced extreme temperatures during 2014–2019, including the four warmest years ever observed. The goal of this study is to evaluate the ecological consequences of that warming event, across multiple trophic levels and taxa. We tested for evidence that observed sea surface temperature (SST) anomalies were outside the envelope of natural climate variability in order to evaluate the risk of novel ecosystem configurations. We also tested for state changes in shared trends of climate ( n = 11) and biology ( n = 48) time series, using a Bayesian implementation of Dynamic Factor Analysis (DFA). And we tested for evidence of novel ecological relationships during 2014–2019. We found that 3-year running mean SST anomalies during 2014–2019 were outside the range of anomalies from preindustrial simulations in CMIP5 models, indicating that the combined magnitude and duration of the warming event was outside the range of natural variability. A DFA model of climate variability also returned a shared trend in climate time series that was at unprecedented levels during 2014–2019. However, DFA models fit to biology data did not show shared trends of variability at unprecedented levels, and Hidden Markov Models fit to shared trends from the climate and biology models failed to find evidence of shifts to a new ecosystem state during 2014–2019. Conversely, we did find preliminary indications that community responses to SST variability strengthened during 2014–2019 after decades of a mostly neutral relationship. Tests for nonstationary patterns of shared variability suggest that covariance between SST and other ecologically-important climate variables remained weaker than during the 1970s Pacific Decadal Oscillation shift, suggesting the potential for muted ecological responses to the 2014–2019 event. Finally, we found that recent patterns of community variability appear to be highly dissimilar to those associated with the 1970s event, suggesting the potential for novel community states with continued warming. In summary, we find no evidence for wholesale ecosystem reorganization during 2014–2019, though nonstationary relationships among climate and community variables suggest the ongoing possibility of novel patterns of ecosystem functioning with continued warming.