Both Geography and Ecology Contribute to Mating Isolation in Guppies
Bibliographic record
Abstract
Local adaptation to different environments can promote mating isolation--either as an incidental by-product of trait divergence, or as a result of selection to avoid maladaptive mating. Numerous recent empirical examples point to the common influence of divergent natural selection on speciation based largely on evidence of strong pre-mating isolation between populations from different habitat types. Accumulating evidence for natural selection's influence on speciation is therefore no longer a challenge. The difficulty, rather, is in determining the mechanisms involved in the progress of adaptive divergence to speciation once barriers to gene flow are already present. Here, we present results of both laboratory and field experiments with Trinidadian guppies (Poecilia reticulata) from different environments, who do not show complete reproductive isolation despite adaptive divergence. We investigate patterns of mating isolation between populations that do and do not exchange migrants and show evidence for both by-product and reinforcement mechanisms depending on female ecology. Specifically, low-predation females discriminate against all high-predation males thus implying a by-product mechanism, whereas high-predation females only discriminate against low-predation males from further upstream in the same river, implying selection to avoid maladaptive mating. Our study thus confirms that mechanisms of adaptive speciation are not necessarily mutually exclusive and uncovers the complex ecology-geography interactions that underlie the evolution of mating isolation in nature.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".