Sex-related spatial segregation and growth in a dioecious conifer along environmental gradients in northwestern Patagonia
Bibliographic record
Abstract
The separation of sexes in plants (dioecy) implies differences in reproductive biology that in many cases favour sexual dimorphism and spatial segregation. Females of the dioecious conifer Austrocedrus chilensis have higher reproductive effort than males. We examined the spatial distribution of male and female Austrocedrus trees along a range of environmental conditions and sex-related growth patterns in northwestern Patagonia. Males were more abundant on high-radiation slopes (M:F ≈ 1.7), while low-radiation slopes had higher abundances of females (M:F ≈ 0.6). This pattern was consistent and equally strong in mesic and xeric sites along a strong rainfall gradient, suggesting that moisture is not the only triggering factor for tree distribution. Austrocedrus females tended to occupy the moister aspects, but genders were not isolated at large geographical scales, avoiding detrimental effects on species fitness. As evidenced by ring widths, males grew ~100% more per year than females on high-radiation exposures, while on low-radiation aspects, males and females did not differ significantly, suggesting that in moister, low-radiation exposures female trees are able to compensate for the reproductive expenses they have. Alternatively, reproductive effort may differ between male and females on different slopes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".