Synchronous multidecadal fish recruitment patterns in Chesapeake Bay, USA
Bibliographic record
Abstract
Multispecies fish recruitment patterns within Chesapeake Bay were investigated in four fishery-independent survey data sets (one primary and three ancillary data sets) that together span the years 1968–2004. These independently conducted surveys record interannual recruitment variability for 15 ecologically and economically important fish species of the Northeast US Continental Shelf Large Marine Ecosystem. Principal component analyses revealed that the strongest multispecies recruitment pattern (first principal component) present in each data set describes a negative recruitment relationship between anadromous and coastal shelf-spawning species. Among the data sets, the first principal component accounted for 31%–42% of multispecies variance. Locally weighted regression modeling revealed that the decadal-scale variability accounted for 62% of the variance in the primary data set’s first principal component’s (annual) scores, whereas interannual variability accounted for only 38%. Despite strong differences in sampling methods, sampled habitats, and sampling locations, this pattern of antagonistic recruitment between Chesapeake Bay anadromous and shelf-spawning (CBASS) species was synchronously correlated among data sets at both decadal and interannual scales. The CBASS pattern has tended to persist in one mode for periods lasting longer than a decade and tends to reverse sign rather within only 2–3 years. A statistically significant regime shift occurred in 1992, when recruitment in anadromous fishes became favored at the expense of recruitment of shelf-spawning estuarine-dependant fishes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".