Knowledge of spawning phenology may enhance selective barrier passage for wetland obligate fishes
Bibliographic record
Abstract
Abstract Within the Laurentian Great Lakes, many native fishes use wetlands for spawning; however, these areas are also used by non-native common carp (Cyprinus carpio) that may impart negative ecological impacts. There is interest to manage common carp using barriers to decrease passage to specific habitats (e.g., their spawning sites), but these barriers could also exclude native wetland obligate spawners such as largemouth bass (Micropterus salmoides) and northern pike (Esox lucius). Our objective was to determine if differences in phenologies, specifically, spawning movements could be exploited in shallow areas to operate seasonal barriers that are opened and closed to promote selective fragmentation. Using a long-term dataset from the Cootes Paradise Marsh fishway (Hamilton, Ontario), we generated predictive models based on cumulative growing degree day (CGDD) for all three fishes. These models successfully predicted earlier arrival by all species in a warmer year and delayed spawning movements during a cold year, supporting the role of temperature as a driver of interannual variation in spawning movements. We then compared the fishway model predictions to movement timing to spawning habitat within nearby Toronto Harbour; movement data was derived from acoustic telemetry information. We found that the models performed well and predictions were correlated with movements of all three species, but performance was weakest for northern pike. It is our hope that managers could use these predictive models to assist in the operation of seasonal barriers to optimize control of non-native common carp, while minimizing negative impacts for native species that require access to coastal wetlands.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".