Factors determining beetle richness and composition along an altitudinal gradient in the high mountains of the Sierra Nevada National Park (Spain)
Bibliographic record
Abstract
One of the more common and dramatic patterns observed in species abundance and richness is that imposed by altitude. Many non-exclusive factors have been proposed to explain these altitudinal patterns, including climate, habitat structure, productivity, geographical factors, and historical factors. In this study, we investigated the altitudinal trend of beetle richness and diversity, trying to determine the factors affecting beetle diversity and composition along an altitudinal gradient on a high mountain of the Sierra Nevada National Park (Spain). Beetles were sampled every 15 d for 2 y using pitfall traps at 10 altitudinal sites located between 2000 and 3000 m asl. Against predictions, beetle diversity and the standardized richness indices were not correlated with altitude. Instead, lower richness and diversity values were found at middle altitude in the selected altitudinal range. Although a combination of biotic and abiotic factors satisfactorily explained these richness and diversity values, the main factor was plant diversity, which was correlated with all richness and diversity indices. A second factor was temperature; however, the interaction between beetle richness and temperature was evident only when the effect of plant diversity was removed. Multivariate analysis for species composition showed that 2 factors, plant diversity and shrub cover, explained more than 50% of the variance observed. In summary, our results suggest a strong dependence of beetle species on vegetation structure and diversity, although temperature appears to be a complementary factor determining beetle richness and diversity in the high mountains of the Sierra Nevada National Park.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".