Coast‐wide recruitment dynamics of Olympia oysters reveal limited synchrony and multiple predictors of failure
Bibliographic record
Abstract
Recruitment of new propagules into a population can be a critical determinant of adult density. We examined recruitment dynamics in the Olympia oyster (Ostrea lurida), a species occurring almost entirely in estuaries. We investigated spatial scales of interannual synchrony across 37 sites in eight estuaries along 2,500 km of Pacific North American coastline, predicting that high vs. low recruitment years would coincide among neighboring estuaries due to shared exposure to regional oceanographic factors. Such synchrony in recruitment has been found for many marine species and some migratory estuarine species, but has never been examined across estuaries in a species that can complete its entire life cycle within the same estuary. To inform ongoing restoration efforts for Olympia oysters, which have declined in abundance in many estuaries, we also investigated predictors of recruitment failure. We found striking contrasts in absolute recruitment rate and frequency of recruitment failure among sites, estuaries, and years. Although we found a positive relationship between upwelling and recruitment, there was little evidence of synchrony in recruitment among estuaries along the coast, and only limited synchrony of sites within estuaries, suggesting recruitment rates are affected more strongly by local dynamics within estuaries than by regional oceanographic factors operating at scales encompassing multiple estuaries. This highlights the importance of local wetland and watershed management for the demography of oysters, and perhaps other species that can complete their entire life cycle within estuaries. Estuaries with more homogeneous environmental conditions had greater synchrony among sites, and this led to the potential for estuary-wide failure when all sites had no recruitment in the same year. Environmental heterogeneity within estuaries may thus buffer against estuary-wide recruitment failure, analogous to the portfolio effect for diversity. Recruitment failure was correlated with lower summer water temperature, higher winter salinity, and shorter residence time: all indicators of stronger marine influence on estuaries. Recruitment failure was also more common in estuaries with limited networks of nearby adult oysters. Large existing oyster networks are thus of high conservation value, while estuaries that lack them would benefit from restoration efforts to increase the extent and connectivity of sites supporting oysters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".