Spatiotemporal interactions of native and introduced salmonid top predators in a large lake: implications for species restoration
Bibliographic record
Abstract
Animal interactions are an integral part of a community’s function, with influences ranging from the spatiotemporal habitat use of species to population effects to ecosystem management. Numerous non-native species are established or maintained through stocking in freshwater ecosystems with the potential to affect restoration of native species. Using acoustic telemetry, this study quantified the spatiotemporal co-occurrence of the native top-predator lake trout (Salvelinus namaycush) with non-native Chinook salmon (Oncorhynchus tshawytscha) in Lake Ontario over 2.5 years. Core home range overlap was observed during the summer, with depth acting as a mechanism of segregation, but with potential for interactions during vertical exploration. Fine-scale individual pairwise interactions confirmed the home range results. No horizontal overlap was observed during the winter and spring, but confidence was lower due to poor instrument coverage in deeper water, which the two species may frequent in these seasons. These results demonstrate the importance of depth in understanding fish interactions and highlight the usefulness of considering pairwise species interactions for understanding ecosystem community function to resource managers with multiple projects involving both native and non-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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".