How Facilitation May Interfere with Ecological Speciation
Bibliographic record
Abstract
Compared to the vast literature linking competitive interactions and speciation, attempts to understand the role of facilitation for evolutionary diversification remain scarce. Yet, community ecologists now recognize the importance of positive interactions within plant communities. Here, we examine how facilitation may interfere with the mechanisms of ecological speciation. We argue that facilitation is likely to (1) maintain gene flow among incipient species by enabling cooccurrence of adapted and maladapted forms in marginal habitats and (2) increase fitness of introgressed forms and limit reinforcement in secondary contact zones. Alternatively, we present how facilitation may favour colonization of marginal habitats and thus enhance local adaptation and ecological speciation. Therefore, facilitation may impede or pave the way for ecological speciation. Using a simple spatially and genetically explicit modelling framework, we illustrate and propose some first testable ideas about how, when, and where facilitation may act as a cohesive force for ecological speciation. These hypotheses and the modelling framework proposed should stimulate further empirical and theoretical research examining the role of both competitive and positive interactions in the formation of incipient species.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".