Testing for synchrony in recruitment among four Lake Michigan fish species
Bibliographic record
Abstract
In the Great Lakes region, multiple fish species display intraspecific spatial synchrony in recruitment success, with interannual climate variation hypothesized as the most likely driver. In Lake Michigan, we evaluated whether climatic or other physical variables could also induce spatial synchrony across multiple species, including bloater (Coregonus hoyi), rainbow smelt (Osmerus mordax), yellow perch (Perca flavescens), and alewife (Alosa pseudoharengus). The residuals from stock–recruitment relationships revealed yellow perch recruitment to be correlated with recruitment of both rainbow smelt (r = 0.37) and alewife (r = 0.36). Across all four species, higher than expected recruitment occurred in 5 years between 1978 and 1987 and then switched to lower than expected recruitment in 5 years between 1996 and 2004. Generalized additive models revealed warmer spring and summer water temperatures and lower wind speeds corresponded to higher than expected recruitment for the nearshore-spawning species, and overall variance explained ranged from 14% (yellow perch) to 61% (alewife). For all species but rainbow smelt, higher recruitment also occurred in extremely high or low years of the North Atlantic Oscillation index. Future development of indices that describe the physical Great Lakes environment could improve understanding of how climate can synchronize fish populations within and across species.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".