Spatial and Temporal Variation in the Life‐History Traits of Yellow Perch ( <i>Perca flavescens</i> ) in the Canadian Waters of Lake Huron
Bibliographic record
Abstract
ABSTRACT The yellow perch ( Perca flavescens ) is an economically important fish species in the Laurentian Great Lakes of North America. The largest commercial harvests occur in Lakes Erie and Huron. In the Canadian waters of Lake Huron, the yellow perch is managed based on 17 spatially distinct management units established decades ago. Despite being the basis for commercial harvest quotas, it is unknown if management units reflect current yellow perch population structure, or if yellow perch life‐history traits changed following a major ecosystem shift associated with invasive mussels in the early 2000s. We calculated life‐history trait parameter values (size at maturity, age at maturity, maximum size, lifespan and growth rate) for female yellow perch across the Canadian portion of Lake Huron in recent (2009–2018, 9264 fish) and historical (1990–1999, 3540 fish) timeframes. We spatially compared recent life‐history trait values and found four discrete clusters of yellow perch driven by latitudinal variation in age at maturity and maximum size, and longitudinal variation in maximum size and lifespan. The depth of capture was an important influence on yellow perch growth rate. We compared recent and historical life‐history trait values and found no temporal variation before and after dreissenid mussel invasion. Our findings demonstrate significant spatial variation in yellow perch life‐history traits but, over spatial scales, much larger than the current management units. Correspondingly, life‐history trait values alone are likely not sufficient for understanding population subdivision in the Canadian waters of Lake Huron.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".