Low Predictability of Colour Polymorphism in Introduced Guppy (Poecilia reticulata) Populations in Panama
Bibliographic record
Abstract
Colour polymorphism is a recurrent feature of natural populations, and its maintenance has been studied in a range of taxa in their native ranges. However, less is known about whether (and how) colour polymorphism is maintained when populations are removed from their native environments, as in the case of introduced species. We here address this issue by analyzing variation in colour patterns in recently-discovered introduced populations of the guppy (Poecilia reticulata) in Panama. Specifically, we use classic colour analysis to estimate variation in the number and the relative area of different colour spots across low predation sites in the introduced Panamanian range of the species. We then compare this variation to that found in the native range of the species under low- and high predation regimes. We found aspects of the colour pattern that were both consistent and inconsistent with the classical paradigm of colour evolution in guppies. On one hand, the same colours that dominated in native populations (orange, iridescent and black) were also the most dominant in the introduced populations in Panama. On the other, there were no clear differences between either introduced-low and native low- and high predation populations. Our results are therefore only partially consistent with the traditional role of female preference in the absence of predators, and suggest that additional factors could influence colour patterns when populations are removed from their native environments. Future research on the interaction between female preference and environmental variability (e.g. multifarious selection), could help understand adaptive variation in this widely-introduced species, and the contexts under which variation in adaptive traits parallels (or not) variation in the native range.
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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 | 0.001 |
| 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".