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Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.

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.