The speed of ecological speciation
Bibliographic record
Abstract
Summary Adaptation can occur on ecological time‐scales (contemporary evolution) and adaptive divergence can cause reproductive isolation (ecological speciation). From the intersection of these two premises follows the prediction that reproductive isolation can evolve on ecological time‐scales. We explore this possibility in theory and in nature. Finding few relevant studies, we examine each in some detail. Theory : Several models have demonstrated that ecological differences can drive the evolution of partial reproductive barriers in dozens to hundreds of generations. Barriers likely to evolve quickly include dispersal rate, habitat preference and selection against migrants/hybrids. Plants : Adjacent populations adapting to different fertilizer treatments or to mine tailings can develop reproductive barriers within at least 100 generations. These barriers include differences in flowering time and selection against migrants/hybrids. Invertebrates : Populations on native and introduced host plants can manifest reproductive barriers in dozens to hundreds of generations. These barriers include local host preference and selection against migrants/hybrids. Vertebrates : Salmon adapting to divergent breeding environments can show restricted gene flow within at least 14 generations. Birds evolving different migratory routes can mate assortatively within at least 10–20 generations. Hybrid sculpins can become isolated from their ancestral species within at least 20–200 generations. Ecological speciation can commence within dozens of generations. How far it goes is an important question for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".