Diversity and phylogenetic community structure across elevation during climate change in a family of hyperdiverse neotropical beetles (Staphylinidae)
Bibliographic record
Abstract
Environmental stress from abiotic conditions imposes physiological limits on individuals within communities, and these stressful conditions can act as a filter on the species present in any given environment. Such abiotic stressors can reduce a community's diversity and make its composition more phylogenetically clustered. Using a decade of staphylinid beetle (Staphylinidae, Coleoptera, rove beetles) collections made across a 1500 m elevation gradient in northwestern Costa Rica (2008–2017) we asked what species lived there, how large and overlapping were the communities across this gradient, and what relationship was there between elevation and diversity. Using DNA barcodes for identification and phylogenetic estimates of community structure, we found high turnover across elevation, and that staphylinid diversity increased linearly with elevation. Because of this, we found staphylinid diversity was negatively related to surface area and temperature, and positively with precipitation. We suggest that historical biogeography and contemporary environmental stress have combined to produce these observed patterns. The forests in which these beetles are found are heating and drying rapidly and our finding that diversity increases with elevation suggests that there will be catastrophic biodiversity loss in the coming decades.
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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 | 0.000 |
| 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".