Predicting non‐native fish dispersal under conditions of climate change: case study in <scp>E</scp>ngland of dispersal and establishment of pumpkinseed <i><scp>L</scp>epomis gibbosus</i> in a floodplain pond
Bibliographic record
Abstract
Abstract Predictions of future climate change include shifts in patterns of precipitation, evapotranspiration and water run‐off, resulting in increased periods of drought as well as variability and intensity of rainfall events. In the United Kingdom, the non‐native North American sunfish, pumpkinseed Lepomis gibbosus (L.), is expected to benefit from these changes. We examine how hydrological variability induced by predicted changes in climate will affect the dispersal and spread of pumpkinseed in England by: (i) determining the relationship between discharge regime and pumpkinseed propagule pressure; (ii) examining a newly‐established pumpkinseed population following a flood event in 2007; and (iii) comparing the growth and life‐history traits of this new population with fish collected from the source population to demonstrate how the pumpkinseed's life‐history plasticity contributes to its success as a coloniser. Using Bayesian modelling, we determined that the number of pumpkinseed escapees is likely to increase with increasing discharge. The newly‐established pumpkinseed population showed fast juvenile growth, early age at maturity and small size at maturity. These traits differed significantly from the source population, specifically total length (TL) means at ages 1 and 2 were significantly greater in the new population, whereas TL at age 4 was significantly greater in the source population, and a significantly higher proportion of mature females were found at smaller size classes in the newly established pumpkinseed population. This study demonstrates the potential link between hydrological variability (current and future) and the dispersal of non‐native pumpkinseed, leading to the establishment of new 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".