Seasonal spatial ecology of Lake Trout in Lake Erie
Bibliographic record
Abstract
Abstract Objective Lake Trout Salvelinus namaycush are native coldwater apex predators that play an important role in maintaining ecosystem functionality and diversity in the Laurentian Great Lakes. Following population collapses, rehabilitation efforts were widely initiated in the Great Lakes to reestablish self-sustaining Lake Trout populations. Lake Erie may pose a challenge to these rehabilitation efforts due to limited availability of appropriate oxythermal habitat. Our goal was to investigate seasonal habitat use of adult Lake Trout in Lake Erie to inform management and rehabilitation efforts. Methods We used acoustic telemetry in Lake Erie, which was equiped with a lake-wide acoustic receiver grid, to quantify Lake Trout seasonal region occupancy, dispersal distances, bottom depth occupancy, space use extent, and space use overlap. Result We found that 32% of fish tagged in the eastern basin and all fish from the western basin dispersed more than 100 km from their tagging location, which represents a greater proportion of the population moving long distances than what has been previously documented in the Great Lakes. During stratification, Lake Trout were detected almost exclusively in the offshore eastern basin in areas where water depth exceeded 25 m. During nonstratified seasons, fish used other regions of the lake, occupying areas of highly variable depths. During fall, most fish tagged in the eastern basin occupied habitat along the southern shore of the eastern basin. Fish tagged in the western basin returned to this region in the fall of subsequent years despite occupying the offshore eastern basin during stratification and having depth occupancy, home range size, and overlap similar to that of eastern basin-tagged fish. Fish size was positively correlated with receiver depth during winter and spring, and with home range overlap during spring and summer. Conclusion The results of this study can begin to inform management decisions regarding stocking locations, harvest regulations, and habitat restoration to facilitate the continued rehabilitation of this important native 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 | 0.000 | 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 | 0.000 | 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".