Synchrony in larval yellow perch abundance: the influence of the Moran Effect during early life history
Bibliographic record
Abstract
Recruitment may vary substantially in fish populations, which can drive not only adult population characteristics but also the dynamics of fishes dependent on the species of interest and recreational fisheries for these species. However, spatiotemporal trends in population fluctuations and potential drivers of recruitment variability are poorly understood. Therefore, we used a long-term (2000–2014) data set to estimate the extent of spatial synchrony in larval abundance and factors influencing variability in recruitment of yellow perch (Perca flavescens). Contrary to the prevailing paradigm that spatial synchrony in population fluctuations (i.e., recruitment) is typically absent or occurs at small spatial scales (<50 km) for freshwater species, abundance of larval yellow perch was synchronous among spatially segregated systems across a geographic scale of at least 180 km. Additionally, variation in larval yellow perch density was influenced by spatially-correlated climatic and hydrological variables (indicative of the Moran Effect). Results ultimately broaden the scale at which factors were previously thought to influence recruitment of freshwater fishes and provide important insight to patterns and processes that structure yellow perch populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".