Rarity and reproductive biology: habitat specialists reveal a complex relationship
Bibliographic record
Abstract
Understanding how species with historically fragmented populations are able to persist will provide insights into which factors may be important for the maintenance of newly fragmented populations. Plants with fragmented and isolated populations, such as habitat-specialist (HS) species, are likely less attractive to pollinators and may have adaptive traits that compensate for these distributional challenges, such as larger flowers and more specialized pollination systems. If they do not have these adaptations, HS species are predicted to have lower reproductive success and be more pollen limited than widespread species. Here, I test three predictions concerning differences in reproductive traits that are known to affect attractiveness to pollinators, pollen receipt, and reproductive success, by comparing three HS species to congeneric species with broader habitat use (HT, habitat tolerators). Two of the three HS species lend partial support to the predictions that HS species have larger floral displays and more specialized pollination systems. The third HS species did not have either of these traits but did have lower relative seedset compared to its matched HT. These results provide preliminary support for a positive relationship between habitat specificity and pollination specialization, and for the role of low fecundity in contributing to range restriction of HS species.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".