Bibliographic record
Abstract
Can speciation occur in a single population when different types of resources are available, in the absence of any geographical isolation, or any spatial or temporal variation in selection? The controversial topics of sympatric speciation and ecological speciation have already stimulated many theoretical studies, most of them agreeing on the fact that mechanisms generating disruptive selection, some level of assortment, and enough heterogeneity in the available resources, are critical for sympatric speciation to occur. Few studies, however, have combined the three factors and investigated their interactions. In this article, I analytically derive conditions for sympatric speciation in a general model where the distribution of resources can be uni- or bimodal, and where a parameter controls the range of resources that an individual can exploit. This approach bridges the gap between models of a unimodal continuum of resources and Levene-type models with discrete resources. I then test these conditions against simulation results from a recently published article (Thibert-Plante & Hendry, 2011, J. Evol. Biol. 24: 2186-2196) and confirm that sympatric ecological speciation is favoured when (i) selection is disruptive (i.e. individuals with an intermediate trait are at a local fitness minimum), (ii) resources are differentiated enough and (iii) mating is assortative. I also discuss the role of mating preference functions and the need (or lack thereof) for bimodality in resource distributions for diversification.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".