Quantifying the spatial scale of common carp (<i>Cyprinus carpio</i>) recruitment synchrony
Bibliographic record
Abstract
Understanding spatial and temporal fluctuations in animal populations remains a central theme in ecology. Here, we investigated the extent of common carp (Cyprinus carpio) recruitment synchrony across North America in relation to a suite of climatic conditions. Common carp were collected from 21 populations up to a linear distance of 2300 km between the most southern and northern locations. Age-frequency histograms were used to estimate year-class strength, and correlation coefficients were used to evaluate synchrony among populations and environmental variables. We then evaluated relationships between common carp recruitment and winter growing degree-days (GDD), summer GDD, precipitation, wind events, and the El Niño Southern Oscillation Index (ENSO). Common carp recruitment was synchronous up to 756 km but asynchronous at larger scales. Winter and summer GDD, precipitation, and wind were also synchronous among locations up to 1640 km apart. Summer GDD appeared most influential to common carp recruitment but varied across latitudes, with negative effects identified at low latitudes and positive effects identified at higher latitudes. Our results provide new insights into the spatial scale of recruitment synchrony of a non-native freshwater fish and indicate that climatic conditions at local to regional scales likely influence recruitment patterns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".