Spawning Ecology of the Largemouth Bass (Micropterus nigricans) in the Bay of Quinte, Lake Ontario
Bibliographic record
Abstract
Largemouth bass (Micropterus nigricans) are widespread nearshore predators that play a key role in shaping aquatic community structure across much of North America. Their ecological importance, coupled with their status as one of the continent’s most valuable sport fish species, underscores the need to understand the factors that influence their reproductive success. Despite extensive research, gaps remain in our understanding of their spawning dynamics in large freshwater systems, such as the Bay of Quinte, an embayment of Lake Ontario. This study used fine-scale acoustic telemetry, water temperature data, and field observations to investigate the spawning behaviour and spatial ecology of largemouth bass in the Bay of Quinte during 2023 and 2024. Water temperature data revealed year-specific trends, while the cumulative degree days at which spawning began and completed were similar between years, indicating that differences in the rate of temperature accumulation influenced the timing and duration of spawning activities. Both telemetry and snorkelling methodologies were effective for independently characterizing the spawning period, and their combined use improved estimates of spawning onset and completion by compensating for methodological limitations inherent to each approach. Sex-specific differences in space use and activity during spawning emphasized the need to account for sex in behavioural studies. Additionally, later-spawning individuals had higher nest success. This research demonstrates the value of integrating telemetry and environmental data to improve understanding of largemouth bass spawning in large, dynamic systems, such as the Bay of Quinte.
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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 | 0.001 |
| 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".