Differential Responses of Eelgrass and Macroalgae in Pacific Northwest Estuaries Following an Unprecedented NE Pacific Ocean Marine Heatwave
Bibliographic record
Abstract
The 2013-2016 northeast Pacific Ocean marine heatwave (MHW) had myriad impacts on marine communities, but little is known about how this event affected coastal estuaries. We examined the extent to which elevated temperatures associated with the offshore MHW were observed in four estuaries (Willapa Bay, Washington and Netarts Bay, Yaquina Bay, and Coos Bay, Oregon) and the responses of macrophytes at marine dominated sites within these estuaries. Records of eelgrass ( Zostera marina ), ulvoid macroalgae ( Ulva and Enteromorpha spp.), and environmental characteristics, including water temperature, were analyzed over three periods: before (2006-2010), during (2015-2016), and after (2017-2019) the MHW. During the MHW, all four estuaries experienced “estuarine heatwaves” (anomalous warm water events) at monitoring stations and there was an associated decline in macroalgae biomass. In northern estuaries (Willapa and Netarts bays) where water temperatures are normally higher and estuaries are shallower, aboveground eelgrass biomass declined and did not recover for at least three years after the MHW. In southern estuaries (Yaquina and Coos bays), where water temperatures are normally colder and estuaries are deeper, the MHW was associated with a neutral, or temporarily positive, change in aboveground eelgrass biomass. Our analysis supports the hypothesis that upwelling intensity and estuarine depth determined the severity of MHW impacts on macrophytes at marine dominated sites, and that the colder and deeper estuaries in the south may serve as refuges for eelgrass but not macroalgae. We suggest that estuary-specific responses were predictable given the drivers of macrophyte abundance at marine dominated sites, and that baseline ocean upwelling and estuarine temperature can be used to anticipate macrophyte responses at marine dominated sites to MHWs in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".