Movement Ecology of a Potamodromous Top Predator in a Large Lake: Synchrony and Coexistence of Distinct Migratory Patterns
Bibliographic record
Abstract
Abstract Migrations are a critical component of the life histories of many highly mobile animals. Potamodromous migrations that occur within large lakes are poorly understood for most species. This lack of understanding hampers restoration efforts and adaptive management because the movement of species and underlying patterns and mechanisms help scientists identify important habitats and quantify species’ roles in the ecosystem. This study quantified the spatiotemporal movements and migratory patterns of Lake Trout Salvelinus namaycush , an iteroparous, potamodromous predator, in eastern Lake Ontario. The lake is highly managed and supports a diverse fish community of native and nonnative species. The movements of 41 Lake Trout were quantified over 2.4 years (December 2016 to April 2019) across a large array of 196 acoustic receivers in eastern Lake Ontario. An analysis of individual movements revealed a potential annual convergence occurring in the fall at a location other than the spawning grounds, followed by a synchronized migration to spawning areas. Consistent with divergent migrations, return migration was asynchronous among individuals but consistent in timing interannually, stretching over a longer period than did prespawning movements and across multiple routes. The data suggest the existence of three groups (i.e., contingents) of Lake Trout with distinct migratory behaviors. This study provides important information on the migratory patterns and routes and a potential staging area for a potamodromous top predator population in a large lake. This information can help managers understand the potential success and implications of employing different rehabilitation strategies, such as diversifying populations of Lake Trout through selective strain stocking in large deep lakes to aid reestablishment across habitats. In addition, our results have the potential to improve community dynamics modeling, understanding of nutrient cycling, and overall ecosystem function of large lakes.
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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 | 0.000 |
| 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 | 0.000 | 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".