Reconstructing half a century of coregonine recruitment reveals species-specific dynamics and synchrony across the Laurentian Great Lakes
Bibliographic record
Abstract
Abstract Understanding how multiple species and populations vary in their recruitment dynamics can elucidate the processes driving recruitment across space and time. Lake Whitefish (Coregonus clupeaformis) and Cisco (C. artedi) are socioecologically important fishes across their range; however, many Laurentian Great Lakes populations have experienced declining, poor, or sporadic recruitment in recent decades. We integrated catch and age data from 38 long-term surveys across each of the Great Lakes and Lake Simcoe, resulting in a combined time series spanning 1960–2019. We estimated Lake Whitefish and Cisco year-class strength (YCS) in each lake using longitudinal mixed-effects regressions of relative cohort abundance. We subsequently quantified interspecific, spatial, and temporal synchrony in YCS using correlation and dynamic factor analyses. Lake Whitefish YCS was positively spatially synchronous on average, and YCS in all six lakes was elevated during the 1980s–1990s. In contrast, Cisco YCS was sporadic, not spatially synchronous, and highly variable around long-term, lake-specific means. YCS was not synchronous between species in any lake. Collectively, our analyses demonstrate that these species exhibit differential recruitment dynamics that may be regulated by species-specific factors. Results from this study can be leveraged in future research on the causes and consequences of cross-species, cross-basin recruitment variability.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".