Species Richness and Phenology of Butterflies Along an Altitude Gradient in the Desert of Northern Chile
Bibliographic record
Abstract
We use butterfly data from an arid subtropical elevation gradient to test temperate-zone hypotheses regarding altitude effects on diversity and phenology. Specifically, species richness is predicted to peak at mid-altitude on arid-zone mountains with opposite temperature and precipitation gradients, and phenological windows of activity are expected to be more synchronized, shorter, and later with altitude.A transect on the Pacific slope of the Andes in northern Chile (23°S, 2400–5000 m a.s.l.) was observed fortnightly between October 2008 and June 2009. The 13 species observed showed high altitudinal and temporal turnover, dividing the transect into three entomofaunal zones that follow well-documented altitudinal vegetation belts. Species richness peaked at mid-altitude in the Puna shrub belt, the zone with highest plant productivity and diversity, supporting McCain’s water-temperature hypothesis. Community-level predictions about phenology were not met: instead, the flight period began earlier at high altitude, presumably due to earlier water availability, and neither synchronization nor duration of flight periods varied consistently with altitude. At the species level, relationships between butterfly phenology and altitude were variable, suggesting no direct effect of altitude but rather complex effects of changing environmental conditions that vary according to individual species’ ecological requirements, host plant use, and lifecycle.
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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 | 0.001 | 0.001 |
| 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".