Foraging Ecology of the Freshwater Apex Predator Lake Trout (Salvelinus namaycush)
Bibliographic record
Abstract
Movement is energetically expensive, yet necessary for energy acquisition by mobile predators, whose foraging over broad spatial scales links food webs in semi-discrete habitat patches. For the cold-water, apex predator lake trout (Salvelinus namaycush), movement into nearshore areas of lakes may be extremely costly because of thermally challenging conditions during summer stratification. However, in lakes without offshore forage fish, forays into these areas may be critical to amass enough energy for growth, fall spawning, and winter survival. In this thesis, I used a novel combination of data – nearshore prey transects, stomach content analysis, three-dimensional lake trout positioning with acoustic telemetry, and accelerometer data – to directly measure the impact of lake trout habitat and food web types on their energy acquisition and expenditure during the habitat-limited summer period in Northwestern Ontario. I studied lake trout behaviour in two small, neighbouring boreal lakes over two summers to ask how their habitat use differs (1) between lakes with different food webs and (2) within lakes between sites of different slopes. The study lakes have similar morphometric qualities but only one contains an offshore forage fish (yellow perch, Perca flavescens). Previous research indicates that steep-sided lakes allow for increased habitat coupling by lake trout during summer, but this is the first work to directly examine the effect of lake morphometry on lake trout foraging within lakes. I used kernel density estimates to show that lake trout tended nearshore with increasing acceleration in the lake without an offshore forage fish, whereas their core areas moved offshore with increasing acceleration in the lake with yellow perch. In nearshore areas, I observed a non-linear effect of slope on acceleration, indicating that lake trout use steep, thermally accessible locations to reach the nearshore fishes found in most of the stomachs analysed. Through stomach content analysis and metabolic calculations, I found that both populations tend towards planktivory and have similar metabolic costs despite behavioural differences, pointing to the energetic limitations of many small, boreal lakes. This thesis expands our understanding of lake trout habitat use during critical foraging events, which is essential for effective aquatic habitat management.
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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 | 0.000 | 0.000 |
| 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".