Incorporating ecological heterogeneity and intraspecific variation differentiates complex interactions between apex predators and stocked fish
Bibliographic record
Abstract
Ecological heterogeneity and intraspecific variation can impact energy flow from the base of the food web to top predators. We evaluated the effect of intraspecific variation on estimates of lake trout ( Salvelinus namaycush) consumption of stocked kokanee (lacustrine sockeye salmon, Oncorhynchus nerka) in a 390 ha oligotrophic lake with two distinct basins. Lake trout stomach content analyses, stable isotope niche space, and catch rates indicated high intraspecific variation in resource use across habitats and basins. Intraspecific variation and ecological heterogeneity were incorporated into two bioenergetics modeling approaches: one lake-wide model, and one partitioned model that accounted for differences in lake trout diet composition and population size across habitats. The ecologically partitioned model highlighted that lake trout consumption was primarily in the epilimnion of one basin, while the lake-wide, unpartitioned model performed similarly but failed to provide ecological context for where that consumption occurred. Incorporating ecological heterogeneity and intraspecific variation into bioenergetic models can more accurately represent top predators foraging patterns across habitats and inform management actions to mitigate impacts to stocked fish.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".