Acoustic Telemetry Provides New Insights into the Ecology of Smallmouth Bass in Eastern Lake Ontario
Bibliographic record
Abstract
Smallmouth Bass (Micropterus dolomieu) are a top predator native to the Laurentian Great Lakes, with a wide distribution in North America. Our understanding of Smallmouth Bass ecology is extensive, although primarily based on research from smaller inland lakes, rivers, and reservoirs. In Lake Ontario, there is an urgent need for additional research on this species due to the apparent declines and invasive species-mediated changes in the Smallmouth Bass population in recent years, documented by provincial and state agencies. A better understanding of the status of Smallmouth Bass populations is especially important in the Southern Great Lakes since pressure from recreational anglers also appears to be increasing. The objective of the present study was to examine whether a novel acoustic telemetry approach could be used to obtain information on the spatial ecology of Smallmouth Bass in the eastern basin of Lake Ontario. Near continuous detections were recorded for eleven Smallmouth Bass, expanding the practical toolbox of passive acoustic telemetry in large lake ecosystems. The present study also provides evidence that this species exhibits local residency to a greater degree than previously understood. Smallmouth Bass depth use and activity rate varied seasonally, with winter characterized by deeper depths and lower vertical and horizontal activity relative to the summer period. Home range size decreased during the overwintering period, with simultaneous shifts in home range centrality towards deeper bathymetric features surrounding the study site. The use of a small-scale passive array to study a nearshore predator in the Great Lakes is a novel approach that has improved our understanding of Smallmouth Bass ecology. The findings of this research also have important implications for assessing, managing and conserving the Smallmouth Bass populations in the Great Lakes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".