Spawning Season Distribution in Subpopulations of Muskellunge in Georgian Bay, Lake Huron
Bibliographic record
Abstract
Abstract Loss of spawning and nursery habitats has been implicated as a major factor in the widespread decline of Muskellunge Esox masquinongy populations in North America. Although there is limited evidence of spawning site fidelity in Great Lakes populations of Muskellunge, such behavior could result in recruitment failure if individuals return each year to spawning sites that have become degraded. We compared the spawning behaviors of individual Muskellunge across three subpopulations in Georgian Bay, Lake Huron, to address the hypothesis that the use of specific spawning sites and spawning site fidelity are independent of the habitat's suitability for successful recruitment. The study regions (southeastern, northeastern, and northern Georgian Bay) have experienced different impacts from human development and sustained low water levels. We radio‐tagged 49 adult Muskellunge and tracked them for up to 3 years (between 2012 and 2015). Sufficient multiyear data were only acquired for 18 individuals in the southeastern region; among those fish, 16 showed fidelity to at least one activity center over 2–3 years. Male Muskellunge occupied significantly smaller activity centers and shallower depths than females during the spawning season. The locations of adult Muskellunge were in close proximity to current and historic nursery sites that had been identified in each region by other studies, supporting the close spatial linkage between spawning habitat and nursery habitat. This study is the first to confirm spawning site fidelity in Georgian Bay Muskellunge, and our results support the spatial association between spawning and nursery habitats. The repeated use of degraded habitat by spawning adults, as appears to be the case in southeastern Georgian Bay, highlights the need to identify and protect spawning and nursery habitats. Received October 1, 2015; accepted February 3, 2016 Published online June 23, 2016
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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 | 0.000 |
| 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".