Thermal preference influences depth use but not biomass of predatory fishes in response to lake morphometry
Bibliographic record
Abstract
ABSTRACT Top predators’ responses to environmental conditions shape food web architecture and influence ecosystem structure and stability. Yet the impacts of fundamental properties like ecosystem size and morphometry on top predators’ behaviour are poorly understood. We examined how lake morphometry impacts the behaviour (inferred by depth use) of three key fish top predators—the cold-adapted lake trout, the cool-adapted walleye, and the warm-adapted smallmouth bass— which can each strongly impact local food web structure. We used catch-per-unit-effort data from nearly 500 boreal lakes of Ontario, Canada to evaluate the role of thermal preference in dictating mean depth of capture and biomass index in response to lake morphometry. We found evidence that thermal preferences influence how species’ depth use and biomass changed with lake size, proportion of littoral area, and maximum lake depth, although we found no relationship with lake shape. However, found no strong evidence that lake morphology influences these species’ biomasses, despite theory that predicts such a relationship. Our results suggest that some aspects of lake morphometry can alter habitat accessibility and productivity in ways that influence the behaviour and biomass of these top predator species depending on their thermal preferences. Our results have implications for how lake food webs expand and contract with lake morphometry and other key abiotic factors. We argue that several key abiotic factors likely drive top predator depth use in ways that may shape local food web structure and play an important role in determining the ultimate fate of ecosystems with environmental change.
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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 | 0.001 |
| 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".